Dynamic adjustment of mobile device based on thermal conditions

ABSTRACT

In some implementations, a mobile device can be configured to monitor environmental, system and user events associated with the mobile device and/or a peer device. The occurrence of one or more events can trigger adjustments to system settings. The mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. In some implementations, before running an application or communicating with a peer device, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device and/or a peer device to ensure a high quality user experience.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/005,964, filed on May 30, 2014, entitled “Dynamic Adjustment ofMobile Device Based on Thermal Conditions,” the content of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The disclosure generally relates to managing system resources based onsystem events.

BACKGROUND

Mobile computing devices are typically battery operated. Some mobilecomputing devices can wirelessly access network resources over cellulardata and/or Wi-Fi network connections. These mobile devices are oftenconstrained by battery capacity and/or limits on cellular data usage.

Some mobile computing devices allow a user to run applications thataccess data from network resources. The user typically invokes anapplication and then must wait for the application to retrieve data fromthe network resources so that the application can present currentupdated content.

SUMMARY

In some implementations, a mobile device can be configured to monitorenvironmental, system and user events. The mobile device can beconfigured to detect the occurrence of one or more events that cantrigger adjustments to system settings.

In some implementations, the mobile device can be configured withpredefined and/or dynamically defined attributes. The attributes can beused by the system to track system events. The attribute events can bestored and later used to predict future occurrences of the attributeevents. The stored attribute events can be used by the system to makedecisions regarding processing performed by the mobile device. Theattributes can be associated with budgets that allow for budgetingresources to support future events or activities on the system.

In some implementations, various applications, functions and processesrunning on the mobile device can submit attribute events. Theapplications, functions and processes can later request forecasts basedon the submitted events. The applications, functions and processes canperform budgeting based on the budgets associated with the attributesand the costs associated with reported events. The applications,functions, and processes can be associated with the operating system ofthe mobile device or third party applications, for example.

In some implementations, the mobile device can be configured to keepfrequently invoked applications up to date. The mobile device can keeptrack of when applications are invoked by the user. Based on theinvocation information, the mobile device can forecast when during theday the applications are invoked. The mobile device can thenpreemptively launch the applications and download updates so that theuser can invoke the applications and view current updated contentwithout having to wait for the application to download updated content.

In some implementations, the mobile device can receive pushnotifications associated with applications that indicate that newcontent is available for the applications to download. The mobile devicecan launch the applications associated with the push notifications inthe background and download the new content. After the content isdownloaded, the mobile device can present a graphical interfaceindicating to the user that the push notification was received. The usercan then invoke the applications and view the updated content.

In some implementations, the mobile device can be configured to performout of process downloads and/or uploads of content for applications onthe mobile device. For example, a dedicated process can be configured onthe mobile device for downloading and/or uploading content forapplications on the mobile device. The applications can be suspended orterminated while the upload/download is being performed. Theapplications can be invoked when the upload/download is complete.

In some implementations, before running an application or accessing anetwork interface, the mobile device can be configured to check batterypower and cellular data usage budgets to ensure that enough power anddata is available for user invoked operations. Before launching anapplication in the background, the mobile device can check usagestatistics to determine whether the application is likely to be invokedby a user in the near future.

In some implementations, attribute event data can be shared betweenmobile devices owned by the same user. The mobile device can receiveevent data from a peer device and make decisions regarding interactionsor operations involving the peer device based on the received eventdata. The event data can be shared as forecasts, statistics, and/or raw(e.g., unprocessed) event data. The mobile device can determine whetherto communicate with the peer device based on the received event data,for example.

Particular implementations provide at least the following advantages:Battery power can be conserved by dynamically adjusting components ofthe mobile device in response to detected events. The user experiencecan be improved by anticipating when the user will invoke applicationsand downloading content so that the user will view updated content uponinvoking an application.

Details of one or more implementations are set forth in the accompanyingdrawings and the description below. Other features, aspects, andpotential advantages will be apparent from the description and drawings,and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a mobile device configured to perform dynamicadjustment of the mobile device.

FIG. 2 illustrates an example process for invoking heuristic processes.

FIG. 3 illustrates a process for adjusting the settings of a mobiledevice using a heuristic process.

FIG. 4 illustrates an example system for performing background fetchupdating of applications.

FIG. 5 illustrates peer forecasting for determining user invocationprobabilities for applications on mobile device 100.

FIG. 6 is a flow diagram of an example process for predictivelylaunching applications to perform background updates.

FIG. 7 is a flow diagram of an example process for determining when tolaunch applications on a mobile device.

FIG. 8 is a flow diagram illustrating state transitions for an entry ina trending table.

FIG. 9 is a block diagram illustrating a system for providing pushnotifications to a mobile device.

FIG. 10 is a flow diagram of an example process for performingnon-waking pushes at a push notification server.

FIG. 11 is a flow diagram of an example process for performingbackground updating of an application in response to a low priority pushnotification.

FIG. 12 is a flow diagram of an example process for performingbackground updating of an application in response to a high prioritypush notification.

FIG. 13 is a block diagram an example system for performing backgrounddownloading and/or uploading of data on a mobile device.

FIG. 14 is flow diagram of an example process for performing backgrounddownloads and uploads.

FIG. 15 illustrates an example graphical user interface (GUI) forenabling and/or disabling background updates for applications on amobile device.

FIG. 16 illustrates an example system for sharing data between peerdevices.

FIG. 17 illustrates an example process for sharing data between peerdevices.

FIG. 18 is a block diagram of an example computing device that canimplement the features and processes of FIGS. 1-17.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION Overview

Described herein is a system architecture for enabling adaptation of amobile device based on various system events to facilitate tradeoffsbetween battery lifetime, power requirements, thermal management andperformance. The system provides the underlying event gatheringarchitecture and a set of heuristic processes that learn from the systemevents to maximize battery life without noticeable degradation in theuser experience. The system monitors system-defined and client-definedattributes and can use the system-defined and client-defined attributesto predict or forecast the occurrence of future events. This system cananticipate the system's future behavior as well as the user'sexpectation of device performance based on dynamically gatheredstatistics and/or explicitly specified user intent. The system candetermine which hardware and software control parameters to set and towhat values to set the parameters in order to improve the userexperience for the anticipated system behavior. The system leveragessystem monitoring and hardware control to achieve an overall improvementin the user experience while extending system and network resourcesavailable to the mobile device. Thus, the system can maximize system andnetwork resources while minimizing the impact to the user experience.

Data Collection—User Centric Statistics

FIG. 1 illustrates an example mobile device 100 configured to performdynamic adjustment of the mobile device 100. In some implementations,mobile device 100 can include a sampling daemon 102 that collects eventsrelated to device conditions, network conditions, system services (e.g.,daemons) and user behavior. For example, sampling daemon 102 can collectstatistics related to applications, sensors, and user input received bymobile device 100 and store the statistics in event data store 104. Thestatistics can be reported to sampling daemon 102 by various clients(e.g., applications, utilities, functions, third-party applications,etc.) running on mobile device 100 using predefined or client-definedattributes reported as events.

Data Collection—Events & Attributes

In some implementations, mobile device 100 can be configured with aframework for collecting system and/or application events. For example,mobile device 100 can be configured with application programminginterfaces (API) that allow various applications, utilities and othercomponents of mobile device 100 to submit events to sampling daemon 102for later statistical analysis.

In some implementations, each event recorded by sampling daemon 102 inevent data store 104 can include an attribute name (e.g., “bundleId”),an attribute value (e.g., “contacts”), anonymized beacon information,anonymized location information, date information (e.g., GMT date ofevent), time information (e.g., localized 24 hour time of event),network quality information, processor utilization metrics, diskinput/output metrics, identification of the current user and/or type ofevent (e.g., start, stop, occurred). For example, the attribute name canidentify the type of attribute associated with the event. The attributename can be used to identify a particular metric being tracked bysampling daemon 102, for example. The attribute value can be a value(e.g., string, integer, floating point) associated with the attribute.The anonymized beacon information can indicate which wireless beacons(e.g., Bluetooth, Bluetooth Low Energy, Wi-Fi, etc.) are in range of themobile device without tying or associating the beacon information to theuser or the device. Similarly, the anonymized location information canidentify the location of the mobile device without tying or associatingthe location information to the user or the device. For example,location information can be derived from satellite data (e.g., globalpositioning satellite system), cellular data, Wi-Fi data, Bluetooth datausing various transceivers configured on mobile device 100. Networkquality information can indicate the quality of the mobile device'snetwork (e.g., Wi-Fi, cellular, satellite, etc.) connection as detectedby mobile device 100 when the event occurred.

In some implementations, the event data for each event can indicate thatthe event occurred, started or stopped. For example, time accounting(e.g., duration accounting) can be performed on pairs of events for thesame attribute that indicate a start event and a stop event for theattribute. For example, sampling daemon 102 can receive a start eventfor attribute “bundleId” having a value “contacts”. Later, samplingdaemon 102 can receive a stop event for attribute “bundleId” having avalue “contacts”. Sampling daemon 102 can compare the time of the startevent to the time of the stop event to determine how long (e.g., timeduration) the “contacts” application was active. In someimplementations, events that are not subject to time accounting can berecorded as point events (e.g., a single occurrence). For example, anevent associated with the “batteryLevel” system attribute that specifiesthe instantaneous battery level at the time of the event can simply berecorded as an occurrence of the event.

Table 1, below, is provides an example of attribute event entriesrecorded by sampling daemon 102 in event data store 104. The first entryrecords a “bundleId” event that indicates that the “contacts”application has been invoked by user “Fred.” This “bundleId” event is astart event indicating that Fred has begun using the contactsapplication. The second entry is a “batteryLevel” event entry thatindicates that the battery level of mobile device 100 is at 46%; thisevent is an occurrence type event (e.g., single point event). The thirdentry is a “personName” event that associated with the value “George.”The “personName” event is used to record the fact that user Fred hasaccessed the contact information for contact “George” in the contactsapplication; this is an occurrence type event. The fourth entry recordsa “bundleId” event that indicates that the “contacts” application hasbeen closed or hidden by user “Fred.” This bundleId event is a stopevent indicating that Fred has stopped using the contacts application.By recording start and stop events for the “bundleId” attribute,sampling daemon 102 can determine that user Fred has used the contactsapplication for 8 minutes on May 12, 2014 based on the timestampscorresponding to the start and stop events. This attribute eventinformation can be used, for example, to forecast user activity relatedto applications on mobile device 100 and with respect to the contactsapplication in particular.

TABLE 1 Network User Attr. Name Value Beacons Location Date Time QualityID State bundleId “contacts” B1, B2 . . . Location1 2014 May 12 1421 8Fred start batteryLevel 46 B1, B2 . . . Location2 2014 May 12 1424 8Fred occur personName “George” B1, B2 . . . Location2 2014 May 12 1426 8Fred occur bundleId “contacts” B1, B2 . . . Location1 2014 May 12 1429 8Fred stop

Predefined Attributes

In some implementations, event data can be submitted to sampling daemon102 using well-known or predefined attributes. The well-known orpredefined attributes can be generic system attributes that can be usedby various applications, utilities, functions or other components ofmobile device 100 to submit event data to sampling daemon 102. While theattribute definition (e.g., attribute name, data type of associatedvalue, etc.) is predefined, the values assigned to the predefinedattribute can vary from event to event. For example, mobile device 100can be configured with predefined attributes “bundleId” for identifyingapplications and “personName” for identifying people of interest. Thevalues assigned to “bundleId” can vary based on which application isactive on mobile device 100. The values assigned to “personName” canvary based on user input. For example, if a user selects an emailmessage from “George,” then the “personName” attribute value can be setto “George.” If a user selects a contacts entry associated with “Bob,”then the “personName” attribute value can be set to “Bob.” When anapplication, utility, function or other component of mobile device 100submits an event to sampling daemon 102 using the predefined attributes,the application, utility, function or other component can specify thevalue to be associated with the predefined attribute for that event.Examples of predefined or well-known system events are described in thefollowing paragraphs.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.bundleId”) that specifies a name oridentifier for an application (e.g., application bundle) installed onmobile device 100. When an application is launched, the applicationmanager 106 (e.g., responsible for launching applications) can use anAPI of the sampling daemon 102 to submit the identifier or name of theapplication (e.g., “contacts” for the contacts application) as the valuefor the “system.bundleId” system attribute. The sampling daemon 102 canrecord the occurrence of the launching of the “contacts” application asan event in event data store 104, for example, along with other eventdata, as described above. Alternatively, the application can use the APIof the sampling daemon 102 to indicate start and stop eventscorresponding to when the application “contacts” is invoked and when theapplication is hidden or closed, respectively. For example, the“bundleId” attribute can be used to record application launch events onmobile device 100. The “bundleId” attribute can be used to recordapplication termination events on mobile device 100. By specifying startand stop events associated with the “bundleId” attribute, rather thanjust the occurrence of an event, the sampling daemon 102 can determinehow long the “contacts” application was used by the user of mobiledevice 100.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.personName”) that specifies a nameor identifier of a user of mobile device 100 or a person of interest tothe user of mobile device 100. For example, upon logging into, waking orotherwise accessing mobile device 100, an event associated with the“personName” attribute can be generated and submitted to sampling daemon102 that identifies the current user of mobile device 100. When the useraccesses data associated with another person, a “personName” attributeevent can be generated and submitted to sampling daemon 102 thatidentifies the other person as a person of interest to the user.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.anonymizedLocation”) that indicatesa location of the mobile device 100. For example, mobile device 100 cangenerate and submit an event to sampling daemon 102 associated with the“anonymizedLocation” attribute that specifies the location of the mobiledevice 100 at the time when the event is generated. The location datacan be anonymized so that the location cannot be later tied orassociated to a particular user or device. The “anonymizedLocation”attribute event can be generated and stored, for example, whenever theuser is using a location-based service of mobile device 100.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.airplaneMode”) that indicates thatthe airplane mode of mobile device 100 is on or off. For example, when auser turns airplane mode on or off, mobile device 100 can generate andsubmit an event to sampling daemon 102 that indicates the airplane modestate at the time of the event. For example, the value of the“airplaneMode” attribute can be set to true (e.g., one) whenairplaneMode is turned on and set to false (e.g., zero) when theairplane mode is off. Sampling daemon 102 can, in turn, store the“airplaneMode” event, including “airplaneMode” attribute value in eventdata store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.cablePlugin”) that indicates thatthe power cable of mobile device 100 is plugged in or is not plugged in.For example, when mobile device 100 detects that the power cable hasbeen unplugged, mobile device 100 can generate an event that indicatesthat the “cablePlugin” attribute value is false (e.g., zero). Whenmobile device 100 detects that the power cable has been plugged intomobile device 100, mobile device 100 can generate an event thatindicates that the “cablePlugin” attribute is true (e.g., one). Mobiledevice 100 can submit the “cablePlugin” event to sampling daemon 102 forstorage in event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.screenLock”) that indicates whetherthe display screen of mobile device 100 is locked or unlocked. Forexample, mobile device 100 can detect when the display screen of mobiledevice 100 has been locked (e.g., by the system or by a user) orunlocked (e.g., by the user). Upon detecting the locking or unlocking ofthe display screen, mobile device 100 can generate an event thatincludes the “screenLock” attribute and set the “screenLock” attributevalue for the event to true (e.g., locked, integer one) or false (e.g.,unlocked, integer zero) to indicate whether the display screen of mobiledevice 100 was locked or unlocked. Mobile device 100 can submit the“screenLock” event to sampling daemon 102 for storage in event datastore 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.sleepWake”) that indicates whethermobile device 100 is in sleep mode. For example, mobile device 100 candetect when mobile device 100 enters sleep mode. Mobile device 100 candetect when mobile device 100 exits sleep mode (e.g., wakes). Upondetecting entering or exiting sleep mode, mobile device can generate anevent that includes the “sleepWake” attribute and sets the attributevalue to true or false (e.g., integer one or zero, respectively) toindicate the sleep mode state of the mobile device 100 at the time ofthe “sleepWake” event. Mobile device 100 can submit the “sleepWake”event to sampling daemon 102 for storage in the event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.backlight”) that indicates whetherthe display of mobile device 100 is lit. The “backlight” attribute canbe assigned a value that indicates the intensity or level of thebacklight. For example, a user of mobile device 100 can adjust theintensity of the lighting (backlight) of the display of mobile device100. The user can increase the intensity of the backlight when theambient lighting is bright. The user can decrease the intensity of thebacklight when the ambient lighting is dark. Upon detecting a change inbacklight setting, mobile device 100 can generate an event that includesthe “backlight” attribute and sets the attribute value to the adjustedbacklight setting (e.g., intensity level). Mobile device 100 can submitthe “backlight” change event to sampling daemon 102 for storage in theevent data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.ALS”) that indicates the ambientlight intensity value as detected by the ambient light sensor of mobiledevice 100. The “ALS” attribute can be assigned a value that indicatesthe intensity or level of the ambient light surrounding mobile device100. For example, the ambient light sensor of mobile device 100 candetect a change in the intensity of ambient light. Mobile device 100 candetermine that the change in intensity exceeds some threshold value.Upon detecting a change in ambient light that exceeds the thresholdvalue, mobile device 100 can generate an event that includes the “ALS”attribute and sets the attribute value to the detected ambient lightintensity value. Mobile device 100 can submit the “ALS” change event tosampling daemon 102 for storage in the event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.proximity”) that indicates the whenthe proximity sensor of mobile device 100 detects that the display ofmobile device 100 is near an object (e.g., the user's face, on a table,etc.). The “proximity” attribute can be assigned a value that indicateswhether the display of the mobile device is proximate to an object(e.g., true, false, 0, 1). For example, the proximity sensor of mobiledevice 100 can detect a change in proximity. Upon detecting a change inproximity, mobile device 100 can generate an event that includes the“proximity” attribute and sets the attribute value to true (e.g., one)when the mobile device 100 is proximate to an object and false (e.g.,zero) when the mobile device 100 is not proximate to an object. Mobiledevice 100 can submit the “proximity” change event to sampling daemon102 for storage in the event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.motionState”) that indicates thetype of motion detected by mobile device 100. The “motionState”attribute can be assigned a value that indicates whether the mobiledevice is stationary, moving, running, driving, walking, etc. Forexample, the motion sensor (e.g., accelerometer) of mobile device 100can detect movement of the mobile device 100. The mobile device 100 canclassify the detected movement based on patterns of motion detected inthe detected movement. The patterns of motion can be classified intouser activities, such as when the user is stationary, moving, running,driving, walking, etc. Upon detecting a change in movement, mobiledevice 100 can generate an event that includes the “motionState”attribute and sets the attribute value to the type of movement (e.g.,stationary, running, walking, driving, etc.) detected. Mobile device 100can submit the “motionState” event to sampling daemon 102 for storage inthe event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.networkQuality”) that indicates thequality of the network connection detected by mobile device 100. The“networkQuality” attribute can be assigned a value that indicates thenetwork throughput value over an n-second (e.g., 1 millisecond, 2seconds, etc.) period of time. For example, mobile device 100 canconnect to a data network (e.g., cellular data, satellite data, Wi-Fi,Internet, etc.). The mobile device 100 can monitor the data throughputof the network connection over a period of time (e.g., 5 seconds). Themobile device can calculate the amount of data transmitted per second(e.g., bits/second, bytes/second, etc.). Upon detecting a change inthroughput or upon creating a new network connection, mobile device 100can generate an event that includes the “networkQuality” attribute andsets the attribute value to the calculated throughput value. Mobiledevice 100 can submit the “networkQuality” event to sampling daemon 102for storage in the event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.batteryLevel”) that indicates aninstantaneous charge level of the internal battery of mobile device 100.The “batteryLevel” attribute can be assigned a value that indicates thecharge level (e.g., percentage) of the battery. For example, mobiledevice 100 can periodically (e.g., every 5 seconds, every minute, every15 minutes, etc.) determine the charge level of the battery and generatea “batteryLevel” event to record the charge level of the battery. Mobiledevice 100 can monitor the battery charge level and determine when thecharge level changes by a threshold amount and generate a “batteryLevel”event to record the charge level of the battery. Mobile device 100 cansubmit the “batteryLevel” event to sampling daemon 102 for storage inthe event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.thermalLevel”) that indicates thethermal level of mobile device 100. For example, the thermal level ofmobile device 100 can be the current operating temperature of the mobiledevice (e.g., degrees Celsius). The thermal level of the mobile device100 can be a level (e.g., high, medium, low, normal, abnormal, etc.)that represents a range of temperature values. For example, mobiledevice 100 can be configured with a utility or function for monitoringthe thermal state of the mobile device 100. Upon detecting a change intemperature or change in thermal level, the thermal utility of mobiledevice 100 can generate an event that includes the “thermalLevel”attribute and sets the attribute value to the operating temperature orcurrent thermal level. Mobile device 100 can submit the “thermalLevel”event to sampling daemon 102 for storage in the event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.energy”) that indicates the energyusage of mobile device 100 over an n-second (e.g., 2 millisecond, 3second, etc.) period of time. For example, when a user invokes afunction (e.g., invocation of an application, illumination of thedisplay, transmission of data, etc.) of mobile device 100, mobile device100 can monitor the energy usage over a period of time that the functionis executing to estimate how much energy each activity or function uses.The mobile device 100 can then generate an event that includes the“energy” attribute and sets the attribute value to the calculatedaverage energy usage. Mobile device 100 can submit the “energy” event tosampling daemon 102 for storage in the event data store 104.

In some implementations, mobile device 100 can be configured with apredefined attribute (e.g., “system.networkBytes”) that indicates thenetwork data usage of mobile device 100 over a n-second (e.g., 2millisecond, 3 second, etc.) period of time. For example, when a userinvokes a function or initiates an operation that requires transmissionof data over a network connection of mobile device 100, mobile device100 can monitor the network data usage over a period of time to estimatehow much network data each activity or function uses or transmits. Themobile device 100 can then generate an event that includes the“networkBytes” attribute and sets the attribute value to the calculatedaverage network data usage. Mobile device 100 can submit the“networkBytes” event to sampling daemon 102 for storage in the eventdata store 104.

Other predefined attributes can include a “system.chargingStatus”attribute having a true/false (e.g., one/zero) attribute valueindicating whether the mobile device 100 is charging its battery, a“system.batteryCapacity” attribute having an attribute value thatindicates the current battery charge (e.g., in mAh, proportional tobatteryLevel), and a “system.devicePresence” attribute having a deviceidentifier (e.g., string) attribute value that tracks the appearances ofpeer devices. For example, the “devicePresence” attribute can be used toforecast the appearance of peer devices when scheduling peer-to-peerdata sharing.

Custom Attributes

In some implementations, client-specific attributes can be dynamicallydefined by clients of sampling daemon 102. For example, instead of usingthe attributes predefined (e.g., in sampling daemon 102 or the operatingsystem) and configured on mobile device 100, clients (e.g., third partyapplications) can dynamically define their own event attributes. Forexample, an email application can dynamically (e.g., at runtime) createa “mailbox” attribute. The email application (“mailapp”) can use an APIof sampling daemon 102 to define the attribute name (e.g.,“mailapp.mailbox”) and the attribute value type (e.g., string, integer,float). Once the client has created (registered) the new customattribute, the client can use the attribute to generate events to bestored in event data store 104. For example, the mailapp can use the“mailbox” attribute to report which mailbox in the email applicationthat the user is accessing. If the user is accessing a “work” mailbox,then the mailapp can create an event using the “mailapp.mailbox”attribute and set the value of the attribute to “work” to record theuser's accessing the “work” mailbox. The sampling daemon 102 and theclient can then use the stored mailbox event information to predict whenthe user is likely to access the “work” mailbox in the future, forexample.

In some implementations, when a client application is removed (e.g.,deleted, uninstalled) from mobile device 100, attributes created by theclient application can be deleted from mobile device 100. Moreover, whenthe client application is removed, event data associated with the clientapplication can be deleted. For example, if mailapp is deleted frommobile device 100, the attribute “mailapp.mailbox” can be deleted frommobile device 100 along with all of the event data associated with themailapp.

Example Event Generating Clients

In some implementations, sampling daemon 102 can receive applicationevents (e.g., “system.bundleId” events) from application manager process106. For example, application manager 106 can be a process that starts,stops and monitors applications (e.g., application 108) on mobile device100. In some implementations, application manager 106 can report startand stop times (e.g., “bundleId” start and stop events) for applicationsrunning on mobile device 100 to sampling daemon 102. For example, when auser invokes or launches an application, application manager 106 cannotify sampling daemon 102 of the application invocation by submitting a“bundleId” start event for the invoked application that specifies thename or identifier of the application. In some implementations,application manager 106 can indicate to sampling daemon 102 that theapplication launch was initiated in response to a push notification,user invocation or a predicted or forecasted user applicationinvocation. When an application terminates, application manager 106 cannotify sampling daemon 102 that the application is no longer running bysubmitting a “bundleId” stop event for the application that specifiesthe name or identifier of the application.

In some implementations, sampling daemon 102 can use the applicationstart and end events (e.g., “bundleId” attribute events) to generate ahistory of usage times per application. For example, the history ofusage times per application can include for each execution of anapplication an amount of time that has passed since the last executionof the application and execution duration. Sampling daemon 102 canmaintain a separate history of user-invoked application launches and/orsystem launched (e.g., automatically launched) applications. Thus,sampling daemon 102 can maintain usage statistics for all applicationsthat are executed on mobile device 100.

In some implementations, sampling daemon 102 can receive power eventsfrom power monitor process 108. For example, power monitor 108 canmonitor battery capacity, discharge, usage, and charging characteristicsfor mobile device 100. Power monitor 108 can determine when the mobiledevice 100 is plugged into external power sources and when the mobiledevice 100 is on battery power. Power monitor 108 can notify samplingdaemon 102 when the mobile device 100 is plugged into external power.For example, power monitor 108 can send a “cablePlugin” event with a“cablePlugin” attribute value of one (e.g., true) to sampling daemon 102when power monitor detects that mobile device 100 is plugged into anexternal power source. The event can include the battery charge at thetime when the external power source is connected. Power monitor 108 cansend “energy” attribute events to sampling daemon 102 to report batteryusage.

In some implementations, power monitor 108 can notify sampling daemon102 when the mobile device 100 is disconnected from external power. Forexample, power monitor 108 can send a “cablePlugin” event with a“cablePlugin” attribute value of zero (e.g., false) to sampling daemon102 when power monitor detects that mobile device 100 is disconnectedfrom an external power source. The message can include the batterycharge at the time when the external power source is disconnected. Thus,sampling daemon 102 can maintain statistics describing the chargingdistribution (e.g., charge over time) of the batteries of the mobiledevice 100. The charging distribution statistics can include an amountof time since the last charge (e.g., time since plugged into externalpower) and the change in battery charge attributable to the charging(e.g., start level of charge, end level of charge).

In some implementations, power monitor 108 can notify sampling daemon102 of changes in battery charge throughout the day. For example, powermonitor 108 can be notified when applications start and stop and, inresponse to the notifications, determine the amount of battery powerdischarged during the period and the amount of charge remaining in thebattery and transmit this information to sampling daemon 102. Forexample, power monitor 108 can send a “system.energy” event to samplingdaemon 102 to indicate the amount of energy consumed over the period oftime during which the application was active.

In some implementations, sampling daemon 102 can receive devicetemperature statistics from thermal daemon 110. For example, thermaldaemon 110 can monitor the operating temperature conditions of themobile device 100 using one or more temperature sensors. Thermal daemon110 can be configured to periodically report temperature changes tosampling daemon 102. For example, thermal daemon 110 can determine theoperating temperature of mobile device 100 every five seconds and reportthe temperature or thermal level of mobile device 100 to sampling daemon102. For example, thermal daemon 110 can send a “system.thermalLevel”event to sampling daemon 102 to report the current operating temperatureor thermal level of mobile device 100. Sampling daemon 102 can store thereported temperatures in event data store 104.

In some implementations, sampling daemon 102 can receive device settingsstatistics from device settings process 112. For example, devicesettings process 112 can be a function or process of the operatingsystem of mobile device 100. Device settings process 112 can, forexample, receive user input to adjust various device settings, such asturning on/off airplane mode, turning on/off Wi-Fi, turning on/offroaming, etc. Device settings process 112 can report changes to devicesettings to sampling daemon 102. Each device setting can have acorresponding predefined event attribute. For example, device settingsprocess 112 can send a “system.airplaneMode” event to sampling daemon102 when the user turns on or off airplane mode on the mobile device100. Sampling daemon 102 can generate and store statistics for thedevice settings based on the received events and attribute values. Forexample, for each time a setting is enabled (or disabled), samplingdaemon 102 can store data that indicates the amount of time that haspassed since the setting was previously enabled and the amount of time(e.g., duration) that the setting was enabled.

Similarly, in some implementations, sampling daemon 102 can receivenotifications from other mobile device 100 components (e.g., devicesensors 114) when other events occur. For example, sampling daemon 102can receive notifications when the mobile device's screen is turned onor off (e.g., “system.backlight” event), when the mobile device 100 isheld next to the user's face (e.g., “system.proximity” event), when acell tower handoff is detected, when the baseband processor is in asearch mode (e.g., “system.btlescan” event), when the mobile device 100has detected that the user is walking, running and/or driving (e.g.,“system.motionState” event). In each case, the sampling daemon 102 canreceive a notification at the start and end of the event. In each case,the sampling daemon 102 can generate and store statistics indicating theamount of time that has passed since the event was last detected and theduration of the event. The sampling daemon 102 can receive other eventnotifications and generate other statistics as described further belowwith respect to specific use cases and scenarios.

Application Events

In some implementations, sampling daemon 102 can receive eventinformation from applications on mobile device 100. For example,applications on mobile device 100 can generate events that includepredefined or dynamically defined attributes to sampling daemon 102 totrack various application-specific events. For example, sampling daemon102 can receive calendar events (e.g., including a“calendar.appointment,” “calendar.meeting,” or “calendar.reminder”attribute etc.) from calendar application 116. The calendar events caninclude a “calendar.appointment,” “calendar.meeting,” or“calendar.reminder” attribute that has values that specify locations,times, or other data associated with various calendar events orfunctions. Sampling daemon 102 can store the attribute name, attributeduration and/or time when the attribute is scheduled to occur, forexample. In some implementations, sampling daemon 102 can receive clockevents (e.g., including a “clock.alarm” attribute) from clockapplication 118. For example, sampling daemon 102 can store theattribute name (e.g., “clock.alarm”) and a value indicating a time whenthe alarm is scheduled to occur. Sampling daemon 102 can receive eventinformation from other applications (e.g., media application, passbookapplication, etc.) as described further below.

Application Statistics

In some implementations, sampling daemon 102 can collect applicationstatistics across application launch events. For example, samplingdaemon 102 can collect statistics (e.g., events, “bundleId” attributevalues) for each application across many invocations of the application.For example, each application can be identified with a hash of itsexecutable's filesystem path and the executable's content's hash so thatdifferent versions of the same application can be handled as distinctapplications. The application hash value can be submitted to samplingdaemon 102 in a “bundleId” event as a value for the “bundleId”attribute, for example.

In some implementations, sampling daemon 102 can maintain a counter thattracks background task completion assertion events for each application.For example, each time an application is run as a background task (e.g.,not visible in the foreground and/or currently in use by the user) theapplication or application manager 106 can notify sampling daemon 102when the application is terminated or is suspended and the samplingdaemon 102 can increment the counter. Sampling daemon 102 can maintain acounter that tracks the cumulative number of seconds across applicationlaunches that the application has run in the background. For example,sampling daemon 102 can analyze “bundleId” start and stop events todetermine when applications are started and stopped and use thetimestamps of start and stop events to determine how long theapplication has run. In some implementations, sampling daemon 102 canmaintain separate counters that count the number of data connections,track the amount of network data traffic (e.g., in bytes), track theduration and size of filesystem operations and/or track the number ofthreads associated with each application. Sampling daemon 102 canmaintain a count of the cumulative amount of time an application remainsactive across application launches, for example. These are just a fewexamples of the types of application statistics that can be generated bysampling daemon 102 based on events and attribute data received bysampling daemon 102 and stored in event data store 104. Other statisticscan be generated or collected, as described further below.

Heuristics

In some implementations, mobile device 100 can be configured withheuristic processes that can adjust settings of device components basedon events detected by sampling daemon 102. For example, heuristicprocesses 120 can include one or more processes that are configured(e.g., programmed) to adjust various system settings (e.g., CPU power,baseband processor power, display lighting, etc.) in response to one ormore trigger events and/or based on the statistics collected orgenerated by sampling daemon 102.

In some implementations, heuristic process 120 can register withsampling daemon 102 to be invoked or activated when a predefined set ofcriteria is met (e.g., the occurrence of some trigger event). Triggerevents might include the invocation of a media player application (e.g.,“bundleId” event) or detecting that the user has started walking,running, driving, etc. (e.g., “motionState” event). The trigger eventcan be generalized to invoke a heuristic process 120 when some property,data, statistic, event, attribute, attribute value etc. is detected inevent data 104 or by sampling daemon 102. For example, a heuristicprocess 120 can be invoked when sampling daemon 102 receives anapplication start notification (e.g., “bundleId” start event thatspecifies a specific application) or a temperature (e.g., “thermalLevel”event) above a certain threshold value. A heuristic process 120 can beinvoked when sampling daemon 102 receives an event associated with aspecified attribute or attribute value. A heuristic process 120 canregister to be invoked when a single event occurs or statistic isobserved. A heuristic process 120 can register to be invoked when acombination of events, data, attributes, attribute values and/orstatistics are observed or detected. Heuristic process 120 can betriggered or invoked in response to specific user input (e.g.,“airplaneMode” event, “sleepWake” event, etc.). When sampling process102 detects the events for which a heuristic process 120 registered,sampling process 102 can invoke the heuristic process 120.

In some implementations, when a heuristic process 120 is invoked, theheuristic process 120 can communicate with sampling daemon 102 toretrieve event data from event data store 104. The heuristic process 120can process the event data and/or other data that the heuristic process120 collects on its own to determine how to adjust system settings toimprove the performance of mobile device 100, improve the user'sexperience while using mobile device 100 and/or avert future problemswith mobile device 100.

In some implementations, heuristic process 120 can make settingsrecommendations that can cause a change in the settings of variousdevice components 122 of mobile device 100. For example, devicecomponents can include CPU, GPU, baseband processor, display, GPS,Bluetooth, Wi-Fi, vibration motor and other components.

In some implementations, heuristic process 120 can make settingsrecommendations to control multiplexer 124. For example, controlmultiplexer 124 can be a process that arbitrates between componentsettings provided by heuristic processes 120 and other processes and/orfunctions of mobile device 100 that influence or change the settings ofthe components of mobile device 100. For example, thermal daemon 110 canbe a heuristics process that is configured to make adjustments to CPUpower, display brightness, baseband processor power and other componentsettings based on detecting that the mobile device 100 is in the middleof a thermal event (e.g., above a threshold temperature). However,heuristic process 120 can be configured to make adjustments to CPUpower, display brightness, baseband processor power and other componentsettings as well. Thus, in some implementations, heuristic process 120and thermal daemon 110 can make settings adjustment recommendations tocontrol multiplexer 124 and control multiplexer 124 can determine whichsettings adjustments to make. For example, control multiplexer 124 canprioritize processes and perform adjustments based on the priority ofthe recommending process. Thus, if thermal daemon 110 is a higherpriority process than heuristic process 120, control multiplexer 124 canadjust the settings of the CPU, display, baseband processor, etc.according to the recommendations of thermal daemon 110 instead ofheuristic process 120.

In some implementations, a mobile device 100 can be configured withmultiple heuristic processes 120. The heuristic processes 120 can beconfigured or reconfigured over the air. For example, the parameters(e.g., triggers, threshold values, criteria, and output) of eachheuristic process 120 can be set or adjusted over the network (e.g.,cellular data connection, Wi-Fi connection, etc.). In someimplementations, new heuristic processes 120 can be added to mobiledevice 100. For example, over time new correlations between triggerevents, statistical data and device settings can be determined by systemdevelopers. As these new correlations are identified, new heuristicprocesses 120 can be developed to adjust system settings to account forthe newly determined relationships. In some implementations, newheuristic processes 120 can be added to mobile device 100 over thenetwork. For example, the new heuristic processes 120 can be downloadedor installed on mobile device 100 over the air (e.g., cellular dataconnection, Wi-Fi connection, etc.).

Example Heuristic Processes

In some implementations, a heuristic process 120 can be configured toadjust system settings of the mobile device 100 to prevent the mobiledevice 100 from getting too hot when in the user's pocket. For example,this hot-in-pocket heuristic process can be configured to register withsampling daemon 102 to be invoked when the mobile device's display isoff (e.g., “system.backlight” event has an attribute value ofzero/false) and when the mobile device 100 is not playing anyentertainment media (e.g., music, movies, video, etc.). When invoked,the hot-in-pocket heuristic can make recommendations to reduce CPU powerand GPU power to reduce the operating temperature of mobile device 100,for example.

In some implementations, heuristic process 120 can be configured toadjust location accuracy when the mobile device's display is not beingused (e.g., “system.backlight” event has an attribute value ofzero/false). For example, if the mobile device's display is not beingused (e.g., the display is turned off, as indicated by the “backlight”attribute event described above), the mobile device 100 cannot displaymap information or directions to the user. Thus, the user is not likelyusing the location services of the mobile device 100 and the locationservices (e.g., GPS location, Wi-Fi location, cellular location, etc.)can be adjusted to use less power. The location accuracy heuristicprocess can register with sampling daemon 102 to be invoked when themobile device's display is off. When invoked, the heuristic process canadjust the power levels of the GPS processor, Wi-Fi transmitter,cellular transmitter, baseband processor or terminate processes used todetermine a location of the mobile device 100 in order to conserve theenergy resources of mobile device 100.

In some implementations, a heuristic process 120 can be configured toadjust the settings of the mobile device's ambient light sensor inresponse to the user's behavior. For example, this user-adaptive ambientlight sensor (ALS) heuristic process can be invoked by sampling daemon102 when sampling daemon 102 receives data (e.g., an “ALS” attributeevent) indicating that the ambient light sensor has detected a change inthe ambient light surrounding mobile device 100, that the ambient lightsensor system has adjusted the brightness of the display and/or that theuser has provided input to adjust the brightness of the display.

When invoked, the user-adaptive ALS heuristic can request additionalinformation from sampling daemon 102 with respect to ALS displayadjustments and user initiated display adjustments to determine if thereis a pattern of user input that indicates that when the ALS adjusts thedisplay brightness up or down and the user adjusts the displaybrightness in the opposite direction (e.g., a “system.ALS” eventfollowed by a “system.backlight” event). For example, the user may ridethe bus or the train to work. The bus lights may be turned on and offduring the ride. The ambient light sensor can detect the change inambient light and increase the display brightness when the lights comeon. Since the lights only come on temporarily, the user may decrease thedisplay brightness when the lights turn off again. This pattern of userinput can be tracked (e.g., through “backlight” attribute events) andcorrelated to time of day, calendar or alarm event entry, or travelpattern by the heuristic process to determine under what circumstancesor context the user adjusts the display brightness in response to an ALSdisplay adjustment. Once the user-adaptive ALS heuristic processdetermines the pattern of input and context, the heuristic process canadjust the settings of the ALS to be more or less aggressive. Forexample, the ALS can be adjusted to check the level of ambient lightmore or less frequently during the determined time of day, calendar oralarm entry, or travel pattern and adjust the display brightnessaccordingly.

The above heuristic processes are a few examples of heuristic processesand how they might be implemented in the system described herein. Otherheuristic processes can be implemented and added to the system as theyare developed over time. For example, additional heuristic processes canbe configured or programmed to adjust CPU, GPU, baseband processors orother components of the mobile device in response to detecting events orpatterns of events related to temperature measurements, user input,clock events (e.g., alarms), calendar events and/or other eventsoccurring and detected on the mobile device.

Example Heuristic Registration and Invocation Processes

FIG. 2 illustrates an example process 200 for invoking heuristicprocesses. At step 202, the sampling daemon 102 can be initialized. Forexample, sampling daemon 102 can be initialized during startup of themobile device 100.

At step 204, the sampling daemon 102 can invoke the heuristic processesconfigured on the mobile device 100 during initialization of thesampling daemon 102. For example, sampling daemon 102 can cause eachheuristic process 120 to execute on mobile device 100 and run throughtheir initialization subroutines.

At step 206, the sampling daemon 102 can receive event registrationmessages from each heuristic process 120. For example, during theinitialization subroutines of the heuristic processes 120, the heuristicprocesses 120 can send information to sampling daemon 102 indicatingwhich attribute events should trigger an invocation of heuristic process120. Sampling daemon 102 can store the registration information in adatabase, such as event data store 104, for example. The registrationinformation can include an identification of the heuristic process(e.g., executable name, file system path, etc.) and event criteria(identification of attributes, attribute values, threshold, ranges,etc.) so that sampling daemon 102 can call the heuristic process 120when the specified event is detected.

At step 208, the sampling daemon 102 can receive attribute event data.For example, sampling daemon 102 can receive attribute event data fromvarious system components, including the application manager 106,sensors 114, calendar 116 and clock 118, as described above.

At step 210, the sampling daemon 102 can compare the received attributeevent data to the heuristic registration data. For example, as attributeevent data is reported to sampling daemon 102, sampling daemon 102 cancompare the event data (e.g., attribute values), or the statisticsgenerated from the event data, to the registration information receivedfrom the heuristic processes 120.

At step 212, the sampling daemon 102 can invoke a heuristic processbased on the comparison performed at step 210. For example, if the eventdata (e.g., attribute data) and/or statistics, meet the criteriaspecified in the heuristic registration data for a heuristic process120, then the sampling daemon 102 can invoke the heuristic process 120.For example, if the event data and/or statistics data cross somethreshold value specified for an event by the heuristic process duringregistration, then the heuristic process can be invoked by samplingdaemon 102. Alternatively, the mere occurrence of a particular attributeevent can cause invocation the heuristic process 120.

FIG. 3 illustrates a process 300 for adjusting the settings of a mobiledevice 100 using a heuristic process 120. At step 302, the heuristicprocess 120 is initialized. For example, the heuristic process 120 canbe invoked by sampling daemon 102 so that the heuristic process 120 canrun through its initialization subroutines. For example, the invocationcan be parameterized to indicate that the heuristic process 120 shouldrun through its initialization subroutines during this invocation.

At step 304, the heuristic process 120 can register with sampling daemon102 for system events. For example, during initialization, the heuristicprocess 120 can send a message to sampling daemon 102 that includes anidentification of events, thresholds, attributes, attribute values orother criteria for invoking the heuristic process 120. When the eventoccurs and/or the criteria are met, sampling daemon 102 can invoke theheuristic process 120.

At step 306, the heuristic process 120 can shut down or terminate. Forexample, the heuristic process 120 is not needed by the system until theregistration criteria are met for the heuristic process 120. Thus, toconserve device resources (e.g., battery power, processing power, etc.),the heuristic process 120 is terminated, shutdown or suspended until itis needed (e.g., triggered by sampling daemon 102).

At step 308, the heuristic process 120 can be restarted. For example,sampling daemon 102 can invoke the heuristic process 120 when samplingdaemon 102 determines that the criteria specified by the heuristicprocess 120 in the registration message have been met.

At step 310, the heuristic process 120 can obtain event data fromsampling daemon 102. For example, once restarted, the heuristic process120 can query sampling daemon 102 for additional attribute event data.The heuristic process 120 can be configured to interact with othersystem resources, processes, sensors, etc. to collect data, as needed.

At step 312, the heuristic process 120 can process event data todetermine component settings. For example, the heuristic process 120 canuse the event data and/or statistics from the sampling daemon 102 and/orthe data collected from other components of the system to determine howto adjust the settings of various components of the mobile device 100.For example, if heuristic process 120 determines that mobile device 100is too hot, heuristic process 120 can determine which power settings ofmobile device 100 will reduce the operating temperature of mobile device100.

At step 314, the heuristic process 120 can transmit the determinedcomponent settings to the control multiplexer 124. For example, thecontrol multiplexer 124 can arbitrate device settings recommendationsreceived from the heuristic process 120 and other system components(e.g., thermal daemon 110). The control multiplexer 124 can then adjustvarious components (e.g., CPU, GPU, baseband processor, display, etc.)of the mobile device 100 according to the received settingsrecommendations.

Forecasting Events

In some implementations, attribute event data stored in event data store104 (e.g., historical data) can be used by sampling daemon 102 topredict the occurrence of future events. For example, “bundleId”attribute events can be analyzed to predict when a user will invokeapplications (e.g., any application or a specific application). The“mailapp.mailbox” event that specifies a particular email folder (e.g.,“mailbox” attribute value set to “work” folder) can be analyzed topredict when a user will use a particular email folder of the “mailapp”application.

Event History Window Specification

In some implementations, an event forecast can be generated based on anevent history window specification. For example, the windowspecification can be generated by a client to specify a time period ofinterest, or recurring time period of interest, upon which the clientwishes to base an event forecast. The window specification can includefour components: a start time, an end time, a recurrence width, and arecurrence frequency. The start time can indicate the date and/or timein history when the window should start. The end time can indicate thedate and/or time in history when the window should end. The recurrencewidth can indicate a block of time (e.g., four hours starting at thestart time) that is of interest to a client. The recurrence frequencycan indicate how frequently the block of time should be repeatedstarting at the start time (e.g., every 8 hours, every two days, everyweek, every two weeks, etc.).

In some implementations, only the events that occur within the specifiedblock of time (e.g., time period of interest) will be analyzed whengenerating an event forecast. For example, if the current date is May13, 2014, a window specification can specify a start date of May 11,2014 at 12:00 pm, an end date of May 12 at 12 pm, a recurrence width of1 hour, and a recurrence frequency of 4 hours. This window specificationwill cause the sampling daemon 102 to analyze event data within each 1hour block (e.g., time period of interest) that occurs every 4 hoursstarting on May 11, 2014 at 12:00 pm and ending on May 12, 2014 at 12:00pm (e.g., block 1: May 11, 2014 at 12:00-1:00 pm; block 2: May 11, 2014at 4:00-5:00 pm; block 3: May 11, 2014 at 8:00-9:00 pm, etc.). In someimplementations, when no recurrence width is specified, the entire timeperiod from the start time to the end time will be analyzed to forecastevents.

In some implementations, sampling daemon 102 can automatically generatean event history window specification. For example, sampling daemon 102can identify patterns in the event history data stored in event datastore 104. If a client requests a forecast for “bundleId” events butdoes not provide a window specification, sampling daemon 102 can, forexample, identify a pattern for the “bundleId” attribute/event thatindicates that applications are typically invoked by the user at8:00-9:00 am, 11:30 am-1:30 pm, and 7:00-11:00 pm. Sampling daemon 102can automatically generate a window specification that includes thosetime periods and excludes other times of day so that a requestedforecast will focus on time periods that are relevant to the requestedattribute. Similarly, sampling daemon 102 can automatically generate anevent history window specification for a particular (e.g., specified)attribute value. For example, if the client requests a forecast for“bundleId” events having an attribute value of “mailapp,” then samplingdaemon 102 can analyze the event history data to identify patterns ofoccurrences related to the “mailapp” value. If the “mailapp” “bundleId”attribute value is recorded in the event history data every day at 10:00am, 12:00 pm and 5:00 pm, then sampling daemon 102 can generate a windowspecification that specifies time periods of interest around those timesof day.

Temporal Forecasts

In some implementations, a temporal forecast can be generated for anattribute or attribute value. The temporal forecast can indicate, forexample, at what time of day an event associated with the attribute orattribute value is likely to occur. For example, a client of samplingdaemon 102 can request a temporal forecast for the “bundleId” attribute(e.g., application launches) over the last week (e.g., last 7 days). Togenerate the forecast, a 24-hour day can be divided into 96 15-minutetimeslots. For a particular timeslot (e.g., 1:00-1:15 pm) on each of thelast seven days, the sampling daemon 102 can determine if a “bundleId”event occurred and generate a score for the timeslot. If the “bundleId”event occurred during the particular timeslot in 2 of the 7 days, thenthe likelihood (e.g., score) that the “bundleId” event will occur duringthe particular timeslot (e.g., 1:00-1:15 pm) is 0.29 (e.g., 2 divided by7). If the “bundleId” event occurred during a different timeslot (e.g.,12:15-12:30 pm) on 4 of the 7 days, then the likelihood (e.g., score)that the “bundleId” event will occur during that timeslot is 0.57 (e.g.,4 divided by 7).

Similarly, a client can request a temporal forecast for a particularattribute value. For example, instead of requesting a temporal forecastfor the “bundleId” attribute (e.g., “bundleId” event), the client canrequest a temporal forecast for “bundleId” events where the “bundleId”attribute value is “mailapp”. Thus, the client can receive an indicationof what time (e.g., 15-minute time-slot) of day the user will likelyinvoke the “mailapp” application.

In some implementations, the temporal forecast can be generated based onan event history window specification. For example, if the clientprovides a window specification that specifies a 4-hour time period ofinterest, the temporal forecast will only generate likelihood scores forthe 15-minute timeslots that are in the 4-hour time period of interest.For example, if the time period of interest corresponds to 12:00-4:00 pmfor each of the last 3 days, then 16 timeslots will be generated duringthe 4 hour period of interest and a score will be generated for each ofthe 16 15 minute timeslots. Scores will not be generated for timeslotsoutside the specified 4 hour time period of interest.

Peer Forecasts

In some implementations, sampling daemon 102 can generate peer forecastsfor attributes. For example, a peer forecast can indicate the relativelikelihoods of values for an attribute occurring during a time period ofinterest relative to all values (e.g., occurrences) of the sameattribute. For example, a client of sampling daemon 102 can request apeer forecast of the “bundleId” attribute over a time period of interest(e.g., 11:00 am-1:00 pm) as specified by a window specificationsubmitted with the request. If, during the time period of interest,“bundleId” events having attribute values “mailapp,” “contacts,”“calendar,” “webbrowser,” “mailapp,” “webbrowser,” “mailapp” occur, thenthe relative likelihood (i.e., score) of “mailapp” occurring is 0.43(e.g., 3/7), the relative likelihood of “webbrowser” occurring is 0.29(e.g., 2/7) and the relative likelihoods for “contacts” or “calendar”occurring is 0.14 (e.g., 1/7).

In some implementations, a client of sampling daemon 102 can request apeer forecast for an attribute. For example, if a client requests a peerforecast for an attribute without specifying a value for the attribute,then sampling daemon 102 will generate a peer forecast and return thevarious probability scores for all values of the attribute within thetime period of interest. Using the example peer forecast above, samplingdaemon 102 will return a list of attribute values and scores to therequesting client, for example: “mailapp”:0.43; “webbrowser”:0.29;“contacts”:0.14; “calendar”:0.14.

In some implementations, a client of sampling daemon 102 can request apeer forecast for an attribute value. For example, the client canrequest a peer forecast for the “bundleId” attribute having a value of“mailapp.” Sampling daemon 102 can generate a peer forecast for the“bundleId” attribute according to the window specification provided bythe client, as described above. For example, the sampling daemon 102 cancalculate the relative likelihood (i.e., score) of “mailapp” occurringis 0.43 (e.g., 3/7), the relative likelihood of “webbrowser” occurringis 0.29 (e.g., 2/7) and the relative likelihoods for “contacts” or“calendar” occurring is 0.14 (e.g., 1/7). Sampling daemon 102 can returna score for the requested “mailapp” value (e.g., 0.43) to the client. Ifthe requested value is not represented in the time period of interest asspecified by the window specification, then a value of zero will bereturned to the client.

Panorama Forecasts

In some implementations, a panorama forecast can be generated to predictthe occurrence of an attribute event. For example, the temporal and peerforecasts described above use the relative frequency of occurrence ofevents for a single attribute or attribute value to predict futureoccurrences of that attribute. This “frequency” forecast type (e.g.,frequency of occurrence) uses only the data associated with theattribute or attribute value specified in the forecast request. Incontrast, a “panorama” forecast can use other data (e.g., location data,beacon data, network quality, etc.) in the event data received for theattribute or attribute value specified in the forecast request. In someimplementations, a panorama forecast can use data from events associatedwith other attributes or attribute values. For example, when a clientrequests a temporal forecast or a peer forecast for a specifiedattribute or attribute value and also specifies that the forecast type(i.e., forecast flavor) is panorama, sampling daemon 102 will analyzeevent data for the specified attribute or attribute value and event datafor other attributes and attribute value to identify correlationsbetween the specified event and other events received by sampling daemon102. For example, a frequency forecast for attribute “bundleId” having avalue “mailapp” might assign a score of 0.4 to the 9:00 am 15-minutetimeslot. However, a panorama forecast might determine that there is astrong correlation between the “mailapp” attribute value and the user'swork location. For example, a panorama forecast might determine that ifthe user is at a location associated with work, the mailapp is invoked90% of the time in the 9:00 am 15-minute timeslot. Thus, sampling daemon102 can assign a higher score (e.g., 0.9) to the “mailapp” forecastscore for the 9:00 am 15-minute timeslot.

Similarly, sampling daemon 102 might find a strong correlation betweenthe “mailapp” “bundleId” attribute value and an occurrence of an eventassociated with the “motionState” attribute value “stationary.” Forexample, sampling daemon 102 can determine that the correlation betweenuse of the mailapp application and mobile device 100 being stationary is95%. Sampling daemon 102 can determine that the correlation between useof the mailapp and mobile device 100 being in motion is 5%. Thus,sampling daemon 102 can adjust the forecast score (e.g., 0.95 or 0.05)for the “mailapp” attribute value for a particular timeslot based onwhether mobile device is moving or stationary.

Scoreboarding—Frequency vs. Panorama

In some implementations, sampling daemon 102 can keep track of whichforecast type is a better predictor of events. For example, whensampling daemon 102 receives an attribute event, sampling daemon 102 cangenerate frequency and panorama forecasts for the attribute or attributevalue associated with the received event and determine which forecasttype would have been a better predictor of the received attribute event.Stated differently, sampling daemon 102 can determine whether thefrequency forecast type or the panorama forecast type would have been abetter predictor of the received attribute event if the forecasts weregenerated immediately before the attribute event was received.

In some implementations, sampling daemon 102 can maintain a scoreboardfor each forecast type (e.g., default, panorama). For example, each timethat sampling daemon 102 determines that the frequency forecast typewould have been a better predictor for a received event, sampling daemon102 can increment the score (e.g., a counter) for the frequency forecasttype. Each time that sampling daemon 102 determines that the panoramaforecast type would have been a better predictor for a received event,sampling daemon 102 can increment the score (e.g., counter) for thepanorama forecast type.

In some implementations, sampling daemon 102 can determine a defaultforecast type based on the scores generated for each forecast type(e.g., frequency, panorama). For example, if the scoreboarding processgenerates a higher score for the panorama forecast type, then panoramawill be assigned as the default forecast type. If the scoreboardingprocess generates a higher score for the frequency forecast type, thenfrequency will be assigned as the default forecast type. When a clientrequests a peer or temporal forecast, the client can specify theforecast type (e.g., panorama, frequency, default). If the client doesnot specify a forecast type, then the default forecast type will be usedto generate peer and/or temporal forecasts.

Attribute Statistics

In some implementations, a client can request that sampling daemon 102generate statistics for an attribute or an attribute value. For example,similar to forecast generation, a client can specify a history windowover which statistics for an attribute or attribute value should begenerated. The sampling daemon 102 will analyze attribute events thatoccur within the specified history window when generating statistics forthe specified attribute or attribute value. The client request canspecify which of the following statistics should be generated bysampling daemon 102.

In some implementations, sampling daemon 102 can generate a “count”statistic for an attribute or attribute value. For example, the “count”statistic can count the number of events associated with the specifiedattribute or attribute value that occur within the specified historywindow.

In some implementations, sampling daemon 102 can generate statisticsbased on attribute values. For example, a client can request andsampling daemon 102 can return the first value and/or the last value foran attribute in the specified history window. A client can request andsampling daemon 102 can return the minimum, maximum, mean, mode andstandard deviation for all values associated with the specifiedattribute within the specified history window. The sampling daemon 102can generate or determine which values are associated with requestedpercentiles (e.g., 10^(th), 25^(th), 50^(th), 75^(th), 90^(th), etc.)

In some implementations, sampling daemon 102 can generate durationstatistics. For example, sampling daemon 102 can determine a durationassociated with an attribute value by comparing an attribute's startevent with the attribute's stop event. The time difference between whenthe start event occurred and when the stop event occurred will be theduration of the event. In some implementations, a client can request andsampling daemon 102 can return the minimum, maximum, mean, mode andstandard deviation for all durations associated with the specifiedattribute or attribute value within the specified history window. Thesampling daemon 102 can generate or determine which duration values areassociated with requested percentiles (e.g., 10^(th), 25^(th), 50^(th),75^(th), 90^(th), etc.)

In some implementations, sampling daemon 102 can generate event intervalstatistics. For example, sampling daemon 102 can determine a timeinterval associated with the arrival or reporting of an event associatedwith an attribute value by comparing a first occurrence of the attributeevent with a subsequent occurrence of an attribute event. The timedifference between when the first event occurred and when the subsequentevent occurred will be the time interval between occurrences of theevent. In some implementations, a client can request and sampling daemon102 can return the minimum, maximum, mean, mode and standard deviationfor all time interval values associated with the specified attribute orattribute value within the specified history window. The sampling daemon102 can generate or determine which interval values are associated withrequested percentiles (e.g., 10^(th), 25^(th), 50^(th), 75^(th),90^(th), etc.).

Keep Applications Up to Date—Fetching Updates

FIG. 4 illustrates an example system 400 for performing background fetchupdating of applications. In some implementations, mobile device 100 canbe configured to predictively launch applications as backgroundprocesses of the mobile device 100 so that the applications can downloadcontent and update their interfaces in anticipation of a user invokingthe applications. For example, the user application launch history data(e.g., “system.bundleId” start events) maintained by sampling daemon 102can be used to forecast (predict) when the user will invoke applicationsof the mobile device 100. These predicted applications can be launchedby the application manager 106 prior to user invocation so that the userwill not be required to wait for a user invoked application to downloadcurrent content and update the graphical interfaces of the applications.

Determining when to Launch Applications—Temporal Forecasts

In some implementations, application manager 106 can request anapplication invocation forecast from sampling daemon 102. For example,sampling daemon 102 can provide an interface that allows the applicationmanager 106 to request temporal forecast of application launches (e.g.,“bundleId” start events) on mobile device 100. Sampling daemon 102 canreceive events (e.g., “bundleId” start events) that indicate when theuser has invoked applications on the mobile device 100, as describedabove. When application manager 106 requests a temporal forecast for the“bundleId” attribute, sampling daemon 102 can analyze the “bundleId”events stored in event data store 104 to determine when during the day(e.g., in which 15-minute timeslot) applications are typically invokedby the user. For example, sampling daemon 102 can calculate aprobability that a particular time of day or time period will include anapplication invocation by a user using the temporal forecastingmechanism described above.

In some implementations, application manager 106 can request a temporalforecast for the “bundleId” attribute from sampling daemon 102 duringinitialization of the application manager 106. For example, applicationmanager 106 can be invoked or launched during startup of mobile device100. While application manager 106 is initializing, application manager106 can request a temporal forecast of application invocations (e.g.,“bundleId” start events) for the next 24 hours. Once the initial 24-hourperiod has passed, application manager 106 can request another 24-hourtemporal forecast. This 24-hour forecast cycle can continue until themobile device 100 is turned off, for example.

In some implementations, sampling daemon 102 can generate an applicationinvocation (e.g., “bundleId” start event) temporal forecast for a24-hour period. For example, sampling daemon 102 can divide the 24-hourperiod into 96 15-minute timeslots. Sampling daemon 102 can determinewhich applications have been invoked and at what time the applicationswere invoked over a number (e.g., 1 to 7) of previous days of operationbased on the application launch history data (e.g., “bundleId” startevent data) collected by sampling daemon 102 and stored in event datastore 104.

In some implementations, when sampling daemon 102 generates a temporalforecast for the “bundleId” attribute, each 15-minute timeslot can beranked according to a probability that an (e.g., any) application willbe invoked in the 15-minute timeslot, as described above in the TemporalForecast section.

Once the application invocation probabilities for each of the 96timeslots is calculated, sampling daemon 102 can select a number (e.g.,up to 64) of the timeslots having the largest non-zero probabilities andreturn information identifying the timeslots to application manager 106.For example, sampling daemon 102 can send application manager 106 a listof times (e.g., 12:00 pm, 1:45 pm, etc.) that correspond to the start of15-minute timeslots that correspond to probable user invoked applicationlaunches (e.g., timeslots that have a score greater than zero).

In some implementations, application manager 106 can set timers based onthe timeslots provided by sampling daemon 102. For example, applicationmanager 106 can create or set one or more timers (e.g., alarms) thatcorrespond to the timeslots identified by sampling daemon 102. When eachtimer goes off (e.g., at 12:00 pm), application manager 106 can wake(e.g., if sleeping, suspended, etc.) and determine which applicationsshould be launched for the current 15-minute timeslot. Thus, the timerscan trigger a fetch background update for applications that are likelyto be invoked by a user within the corresponding timeslot.

In some implementations, other events can trigger a fetch backgroundupdate for applications. For example, application manager 106 canregister interest for various events with sampling daemon 102. Forexample, application manager 106 can register interest in events (e.g.,attributes) related to turning on a cellular radio, baseband processoror establishing a network connection (e.g., cellular or Wi-Fi) so thatapplication manager 106 can be notified when these events occur and cantrigger a background application launch so that the application updatecan take advantage of an active network connection. Unlocking the mobiledevice 100, turning on the display and/or other interactions can triggera background application launch and fetch update, as described furtherbelow. In some implementations, application manager 106 will not triggera background application launch and fetch update if any backgroundupdates were performed within a previous number (e.g., seven) ofminutes.

Determining What Applications to Launch—Peer Forecasts

In some implementations, application manager 106 can request thatsampling daemon 102 provide a list of applications to launch for thecurrent time. For example, when a timer goes off (e.g., expires) for a15-minute timeslot or a triggering event is detected, applicationmanager can request a peer forecast from sampling daemon 102 for the“bundleId” attribute so that sampling daemon 102 can determine whichapplications to launch for the current timeslot. Sampling daemon 102 canthen generate peer forecasts that include a list of applicationidentifiers and corresponding scores indicating the probability thateach application will be invoked by the user at about the current time.

FIG. 5 illustrates peer forecasting for determining user invocationprobabilities for applications on mobile device 100. For example,diagram 500 illustrates peer forecasting for a recent history windowspecification (e.g., previous 2 hours). Diagram 530 illustrates peerforecasting for a daily history window specification (e.g., 4 hourblocks every day for previous 7 days). Diagram 560 illustrates peerforecasting for a weekly history window specification (e.g., 4 hourblock, once every 7 days). In some implementations, sampling daemon 102can perform time series modeling using peer forecasts for differentoverlapping window specifications to determine the user invocationprobabilities for applications on mobile device 100. If an applicationdoes not show up in the peer forecasts, the application can be assigneda zero probability value.

In some implementations, time series modeling can be performed bygenerating peer forecasts for different windows of time. For example,recent, daily and weekly peer forecasts can be generated by based onrecent, daily and weekly event history window specifications. Therecent, daily and weekly peer forecasts can then be combined todetermine which applications to launch at the current time, as describedfurther below.

In some implementations, user invocation probabilities can be generatedbased on recent application invocations. For example, user invocationprobabilities can be generated by performing a peer forecast for the“bundleId” attribute with a window specification that specifies theprevious two hours as the time period of interest (e.g., user initiatedapplication launches within the last two hours).

As illustrated by diagram 500, application launch history data (e.g.,“bundleId” event data) can indicate a number (e.g., four) ofapplications were launched in the previous two hours. For example, thedots and circles can represent applications where the empty circles canrepresent a single particular application (e.g., email, socialnetworking application, etc.) and the empty circles representinvocations of other applications. The peer forecast probability scoreassociated with the particular application using recent history (e.g.,previous 2 hours) can be calculated by dividing the number ofinvocations of the particular application (e.g., 2) by the total numberof application invocations (e.g., 4) within the previous two hours. Inthe illustrated case, the probability associated with the particularapplication using recent application launch history data is 2/4 or 50%.

User invocation probabilities can be generated based on a daily historyof application launches (e.g., which applications were launched at thecurrent time +−2 hours for each of the previous seven days). Forexample, user invocation probabilities can be generated by performing apeer forecast for the “bundleId” attribute with a window specificationthat specifies the current time of day +−2 hours (e.g., 4 hourrecurrence width) as the time period of interest (e.g., user initiatedapplication launches within the last two hours) with a recurrencefrequency of 24 hours (e.g., repeat the recurrence width every 24hours).

Diagram 530 illustrates a daily history of application launches (e.g.,“bundleId” start events) that can be used to determine a user invocationprobability for an application. For example, each box of diagram 530represents time windows (e.g., current time of day +−2 hours) in each ofa number (e.g., 7) of previous days (e.g., as specified in the windowspecification of a peer forecast) that can be analyzed to determine theuser invocation probability (e.g., peer forecast score) for a particularapplication (e.g., empty circle). The probability associated with theparticular application using daily history data can be calculated bydividing the number of invocations of the particular application in allwindows (e.g., 6) by the total number of application invocations in allwindows (e.g., 22). In the illustrated case, the probability associatedwith the particular application using daily launch history data is 6/22or 27%.

User invocation probabilities can be generated based on a weekly historyof application launches (e.g., which applications were launched at thecurrent time +−2 hours seven days ago). For example, user invocationprobabilities can be generated by performing a peer forecast for the“bundleId” attribute with a window specification that specifies thecurrent time of day +−2 hours (e.g., 4 hour recurrence width) as thetime period of interest (e.g., user initiated application launcheswithin the last two hours) with a recurrence frequency of 7 days (e.g.,repeat the recurrence width every 7 days).

Diagram 560 illustrates a weekly history of application launches (e.g.,“bundleId” start events) that can be used to determine a user invocationprobability for an application. For example, if the current day and timeis Wednesday at 1 pm, the user invocation probability (e.g., peerforecast score) for an application can be based on applications launchedduring the previous Wednesday during a time window at or around 1 pm(e.g., +−2 hours). In the illustrated case, the probability associatedwith the particular application (e.g., empty circle) using weeklyapplication launch history data is ¼ or 25%.

In some implementations, the recent, daily and weekly user invocationprobabilities can be combined to generate a score for each application.For example, the recent, daily and weekly probabilities can be combinedby calculating a weighted average of the recent (r), daily (d) andweekly (w) probabilities. Each probability can have an associated weightand each weight can correspond to an empirically determined predefinedimportance of each probability. The sum of all weights can equal one.For example, the weight for probability based on recent launches can be0.6, the weight for the daily probability can be 0.3, and the weight forthe weekly probability can be 0.1. Thus, the combined probability scorecan be the sum of 0.6(r), 0.3(d) and 0.1(w) (e.g.,score=0.6r+0.3d+0.1w).

Referring back to FIG. 4, once the probability score is determined foreach application based on the recent, daily and weekly probabilities,sampling daemon 102 can recommend a configurable number (e.g., three) ofapplications having the highest non-zero probability scores to theapplication manager 106 for launching to perform background fetchdownloads/updates.

In some implementations, sampling daemon 102 can exclude from the “whatto launch” analysis described above applications that do not supportbackground updates (e.g., fetching) application updates, applicationswhere the user has turned off background updates, applications that haveopted out of background updates, and/or whichever application iscurrently being used by the user or is in the foreground on the displayof the mobile device 100 since it is likely that the foregroundapplication is already up to date.

In some implementations, once application manager 106 receives thatrecommended applications from sampling daemon 102, application manager106 can ask sampling daemon 102 if it is ok to launch each of therecommended applications. Sampling daemon 102 can use its localadmission control mechanism (described below) to determine whether it isok for the application manager to launch a particular application. Forexample, application manager 106 can send the “bundleId” attribute withan attribute value that identifies one of the recommended applicationsto sampling daemon 102 and request that sampling daemon 102 performadmission control on the attribute value.

Local Admission Control

In some implementations, sampling daemon 102 can perform admissioncontrol for attribute events on mobile device 100. For example,admission control can be performed on an attribute or attribute value todetermine whether a client application can perform an activity, action,function, event, etc., associated with the attribute. For example, aclient of sampling daemon 102 can request admission of attribute“bundleId” having a value of “mailapp.” In response to receiving theadmission request, sampling daemon can determine whether the client canperform an activity associated with the “mailapp” attribute value (e.g.,execute the “mailapp” application).

In some implementations, admission control can be performed based onbudgets and feedback from voters. For example, when sampling daemon 102receives an admission control request the request can include a costassociated with allowing the attribute event (e.g., launching anapplication, “bundleId” start event). Sampling daemon 102 can check asystem-wide data budget, a system-wide energy budget and/or specificattribute budgets to determine whether the budgets associated with theattribute have enough credits remaining to cover the attribute event. Ifthere is no budget associated with the attribute (e.g., the attribute isnot a budgeted attribute), then the attribute event can be allowed toproceed (e.g., sampling daemon 102 will return an “ok” value in responseto the admission control request). If there is a budget associated withthe attribute and there is not enough credits left in the associatedbudget to cover the cost of the event, then the attribute event will notbe allowed to proceed (e.g., sampling daemon 102 will return an “no”value in response to the admission control request).

If there is a budget associated with the attribute and there is enoughcredits left in the budget to cover the cost of the event, then thevoters will be asked to vote on allowing the attribute to proceed. Ifall voters vote ‘yes,’ then the attribute event will be allowed toproceed (e.g., sampling daemon 102 will return an “ok” value in responseto the admission control request). If any voter votes ‘no,’ then theattribute event will not be allowed to proceed (e.g., sampling daemon102 will return an “no” value in response to the admission controlrequest). Details regarding budgets and voters are described in theparagraphs below.

In some implementations, if an attribute or attribute value has not beenreported in an event to sampling daemon 102 in a period of time (e.g., 7days, one month, etc.) preceding the admission control request, then thesampling daemon 102 can return a “never” value in response to theadmission control request. For example, sampling daemon 102 can generatea temporal or peer forecast to determine when to allow or admit an eventassociated with an attribute or attribute value. For example, there isno need to preempt an event that is not expected to occur (e.g., no needto prefetch data for applications that are not going to be invoked bythe user).

Admission Control—Budgets

In some implementations, sampling daemon 102 can perform admissioncontrol based on budgets associated with attributes or attribute values.For example, sampling daemon 102 can determine whether to allow (e.g.,admit) an activity (e.g., event) associated with an attribute orattribute value based on a budget associated with the attribute orattribute value. In some implementations, sampling daemon 102 candetermine whether it is ok to admit an attribute or attribute valuebased on a system-wide energy budget and/or a system-wide data budgetconfigured for mobile device 100. Sampling daemon 102 can store budgetin accounting data store 402, including counters for keeping track ofremaining data and energy budgets for the current time period (e.g.,current hour). When a client requests admission control be performed foran attribute or attribute value, the client can specify a numberrepresenting the cost of allowing or admitting an event associated withthe attribute or attribute value to occur. If there are enough creditsin the budget associated with the attribute, then the attribute eventwill be voted on by the voters described below. If there are not enoughcredits in the budget associated with the attribute, then the attributeevent will not be allowed to proceed.

System-Wide Energy Budget

In some implementations, sampling daemon 102 can determine whether it isok to admit an attribute or attribute value based on an energy budget.For example, the energy budget can be a percentage (e.g., 5%) of thecapacity of the mobile device's battery in milliamp hours.

In some implementations, the energy budget can be distributed among eachhour in a 24-hour period. For example, sampling daemon 102 can utilizethe battery utilization statistics (e.g., “system.energy” events)collected and stored in event data store 104 to determine a distributionthat reflects a typical historical battery usage for each hour in the24-hour period. For example, each hour can be assigned a percentage ofthe energy budget based on the historically or statistically determinedenergy use distribution or application usage forecast, as describedabove. Each hour will have at least a minimum amount of energy budgetthat is greater than zero (e.g., 0.1%, 1%, etc.). For example, 10% ofthe energy budget can be distributed among hours with no use data andthe remaining 90% of the energy budget can be distributed among activeuse hours according to historical energy or application use. As eachhour passes, the current energy budget will be replenished with theenergy budget for the new/current hour. Any energy budget left over froma previous hour will be added to the current hour's budget.

In some implementations, accounting data store 402 can include a counterfor determining how much energy budget remains available. For example,accounting data store 402 can include one or more counters that areinitialized with the energy budget for the current hour. When the energybudget is used by an attribute event, the energy budget can bedecremented by a corresponding amount. For example, application manager106 can notify sampling daemon 102 when an application is launched orterminated using a “bundleId” start or stop event. In turn, samplingdaemon 102 can notify power monitor 108 when an application is launchedand when the application is terminated. Based on the start and stoptimes, power monitor 108 can determine how much energy was used by theapplication. Power monitor 108 can transmit the amount of power used bythe application (e.g., by submitting a “system.energy” attribute event)to sampling daemon 102 and sampling daemon 102 can decrement theappropriate counter by the amount of power used.

In some implementations, when no energy budget remains for the currenthour, sampling daemon 102 can decline the admission request for theattribute. For example, when the energy budget counters in accountingdata store 402 are decremented to zero, no energy budget remains and noactivities, events, etc., associated with attributes that are tied tothe energy budget can be admitted. If enough energy budget remains forthe current hour to cover the cost of the attribute event, samplingdaemon 102 can return a “yes” value in response to the admission controlrequest and allow the attribute event to proceed.

In some implementations, sampling daemon 102 will not base an admissioncontrol decision on the energy budget when the mobile device 100 isplugged into external power. For example, a remaining energy budget ofzero will not prevent attribute events when the mobile device 100 isplugged into an external power source.

System-Wide Data Budget

In some implementations, sampling daemon 102 can determine whether it isok to admit an attribute based on a data budget. For example, samplingdaemon 102 can determine an average amount of network data consumed bythe mobile device 100 based on statistical data (e.g.,“system.networkBytes” attribute events) collected by sampling daemon 102and stored in event data store 104. The network data budget can becalculated as a percentage of average daily network data consumed by theuser/mobile device 100. Alternatively, the network data budgets can bepredefined or configurable values.

In some implementations, the network data budgets can be distributedamong each hour in a 24-hour period. For example, each hour can beallocated a minimum budget (e.g., 0.2 MB). The remaining amount of thenetwork data budget can be distributed among each of the 24 hoursaccording to historical network data use. For example, sampling daemon102 can determine based on historical statistical data (e.g.,“system.networkBytes” attribute events) how much network data isconsumed in each hour of the day and assign percentages according to theamounts of data consumed in each hour. As each hour passes, the currentdata budget will be replenished with the data budget for the new/currenthour. Any data budget left over from a previous hour can be added to thecurrent hour's data budget.

In some implementations, accounting data store 402 can maintain datacounters for network data budgets. As network data is consumed, the datacounters can be decremented according to the amount of network dataconsumed. For example, the amount of network data consumed can bedetermined based on application start and stop events (e.g., “bundleId”start or stop events) provided to sampling daemon 102 by applicationmanager 106. Alternatively, the amount of network data consumed can beprovided by a process managing the network interface (e.g., networkdaemon 406, background transfer daemon 1302). For example, the networkinterface managing process can report “system.networkBytes” events tosampling daemon 102 that can be correlated to application start and stopevents (e.g., “bundleId” events) to determine how much data anapplication consumes.

In some implementations, sampling daemon 102 can keep track of whichnetwork interface type (e.g., cellular or Wi-Fi) is used to consumenetwork data and determine the amount of network data consumed based onthe network interface type. The amount of network data consumed can beadjusted according to weights or coefficients assigned to each interfacetype. For example, network data consumed on a cellular data interfacecan be assigned a coefficient of one (1). Network data consumed on aWi-Fi interface can be assigned a coefficient of one tenth (0.1). Thetotal network data consumed can be calculated by adding the cellulardata consumed to Wi-Fi data consumed divided by ten (e.g., totaldata=1*cellular data+0.1*Wi-Fi). Thus, data consumed over Wi-Fi willimpact the data budget much less than data consumed over a cellular dataconnection.

In some implementations, when no data budget remains for the currenthour, sampling daemon 102 can respond with a “no” reply to the admissioncontrol request. For example, when the data budget counters inaccounting data store 402 are decremented to zero, no data budgetremains and no activities associated with attributes that are tied tothe data budget will be allowed. If there is enough remaining databudget in the current hour to cover the data cost of the attributeevent, then sampling daemon 102 can respond with a “yes” reply to theadmission control request.

Attribute Budgets

In some implementations, an attribute can be associated with a budget.For example, a predefined attribute or custom (dynamically defined)attribute can be associated with a budget through an API of the samplingdaemon 102. A client (e.g., application, utility, function, third partyapplication, etc.) of the sampling daemon 102 can make a request to thesampling daemon 102 to associate an attribute with a client-definedbudget. The budget can be, for example, a number of credits.

Once the budget is allocated, reported events associated with thebudgeted attribute can indicate a cost associated with the event and thebudget can be decremented according to the specified cost. For example,a predefined system attribute “system.btlescan” can be configured onmobile device 100 to indicate when the mobile device 100 performs scansfor signals from other Bluetooth low energy devices. The Bluetooth LEscan can be run as a background task, for example. The Bluetooth LE scanrequires that the Bluetooth radio be turned on which, in turn, consumesenergy from the battery of mobile device 100. To prevent the BluetoothLE scan from consuming too much energy, the “btlescan” attribute can beassigned a budget (e.g., 24 credits). Every time a “btlescan” event isgenerated and reported to sampling daemon 102, the event can be reportedwith a cost (e.g., 1). The cost can be subtracted from the budget sothat every time the “btlescan” attribute is reported in an event thebudget of 24 is decremented by 1.

In some implementations, the attribute budget can be distributed over atime period. For example, the “btlescan” attribute budget can bedistributed evenly over a 24 hour period so that the “btlescan”attribute can only spend 1 credit per hour. In some implementations, theattribute budget can be replenished at the end of a time period. Forexample, if the period for the “btlescan” attribute budget is 24 hours,then the “btlescan” attribute budget can be replenished every 24 hours.

In some implementations, a budget associated with an attribute can be acan be a subset (e.g., sub-budget) of another budget. For example, abudget for an attribute can be specified as a portion of another budget,such as the system-wide data or system-wide energy budgets describedabove. For example, the “mailapp.mailbox” attribute can be associatedwith a budget that is 5% of the data budget allocated for the system.The “btlescan” attribute can be associated with a budget that is 3% ofthe energy budget allocated for the system. The sub-budget (e.g.,“mailbox” budget) can be tied to the super-budget (e.g., system databudget) such that decrementing the sub-budget also decrements thesuper-budget. In some implementations, if the super-budget is reduced tozero, then the sub-budget is also reduced to zero. For example, if thesystem data budget is at zero, the “mailbox” attribute budget will alsobe zero even if the no events have been reported for the “mailbox”attribute that would decrement the “mailbox” attribute budget.

In some implementations, sampling daemon 102 clients can request thatthe sampling daemon 102 return the amount of budget left for anattribute. For example, a client can make a request to the samplingdaemon 102 for the budget remaining for the “btlescan” attribute. Ifthree of 24 budgeted credits have been used, then sampling daemon 102can return the value 21 to the requesting client.

In some implementations, a client can report an event that costs aspecified number of budgeted credits when no credits remain in thebudget for the associated attribute. When sampling daemon 102 receivesan event (e.g., “btlescan” event) that costs 1 credit when there are nocredits remaining in the budget, sampling daemon 102 can decrement thebudget (e.g., −1) and return an error to the client that reported theevent. The error can indicate that the attribute has no budgetremaining, for example.

Attribute Budget Shaping

In some implementations, the attribute budget can be distributed basedon historical usage information. For example, as events are reported fora budgeted attribute, requests (e.g., events associated with a cost) touse the budget for the attribute can be tracked over time. If a budgetof 24 is allocated for the “btlescan” attribute, for example, the budgetcan initially be allocated evenly across a 24-hour period, as describedabove. As events are reported over time for an attribute associated withthe budget, sampling daemon 102 can analyze the reported events todetermine when during the 24-hour period the events are most likely tooccur. For example, sampling daemon 102 can determine that the“btlescan” event frequently happens around 8 am, 12 pm and 6 pm butrarely happens around 2 am. Sampling daemon 102 can use this eventfrequency information to shape the distribution of the “btlescan”atribute's budget over the 24-hour period. For example, sampling daemoncan allocate two budget credits for each timeslot corresponding to 8 am,12 pm and 6 pm and zero budget credits for the timeslot associated with2 am.

Admission Control—Voters

In some implementations, sampling daemon 102 can perform admissioncontrol based on feedback from other software (e.g., plugins, utilities,applications, heuristics processes) running on mobile device 100. Forexample, other software can be configured to work with sampling daemon102 as a voter for admission control. For example, several voters (e.g.,applications, utilities, daemons, heuristics, etc.) can be registeredwith sampling daemon 102 to vote on admission control decisions. Forexample, sampling daemon 102 can be configured to interface with a voterthat monitors the thermal conditions of mobile device 100, a voter thatmonitors CPU usage of mobile device 100 and/or a voter that monitorsbattery power level of mobile device 100. When sampling daemon 102receives an admission control request, each voter (e.g., thermal, CPUand battery) can be asked to vote on whether the activity associatedwith the specified attribute should be allowed. When all voters vote‘yes’, then the attribute will be admitted (e.g., the activityassociated with the attribute will be allowed to happen). When a singlevoter votes ‘no’, then the attribute will not be admitted (e.g., theactivity associated with the attribute will not be allowed). In someimplementations, the voters can be configured as plugin software thatcan be dynamically (e.g., at runtime) added to sampling daemon 102 toprovide additional functionality to the admission control system. Insome implementations, the voters can use the temporal and peerforecasting mechanisms described above when determining whether to admitor allow an event associated with an attribute or attribute value.

Network Daemon

In some implementations, a network daemon 406 can be configured as anadmission control voter. The network daemon 406 can be configured to usea voting API of sampling daemon 102 that allows the network daemon 406to receive voting requests from sampling daemon 102 and provide voting(e.g., yes, no) responses to sampling daemon 102. For example, thenetwork daemon 406 can receive a voting request from sampling daemon 102that includes an attribute and/or attribute value. The network daemon406 can indicate that sampling daemon 102 should not admit or allow anevent associated with an attribute or attribute value when the mobiledevice 100 is connected to a voice call and not connected to a Wi-Finetwork connection, for example. For example, to prevent backgroundupdating processes (e.g., fetch processes) from interfering with orreducing the quality of voice calls, the network daemon 406 will notallow events (e.g., “bundleId” start events) associated with launching abackground updating process when the user is connected to a voice calland not connected to a Wi-Fi connection. Thus, network daemon 406 canreturn a “no” value in response to a voting request when the mobiledevice 100 is connected to a call and not connected to Wi-Fi.

In some implementations, the network daemon 406 can indicate thatsampling daemon 102 should not allow or admit an attribute event whenthe mobile device 100 has a poor quality cellular network connection. Apoor quality cellular connection can be determined when transfer rateand/or throughput are below predefined threshold values. For example, ifthe mobile device 100 has a poor quality cellular network connection andis not connected to Wi-Fi, the network daemon 406 can prevent admissionor execution of an attribute event that will waste battery energy andcellular data by using the poor quality network connection (e.g.,launching an application that will attempt to download or upload dataover a poor cellular connection) by returning a “no” value when samplingdaemon 102 makes a voter request.

In some implementations, when network daemon 406 does not haveinformation that indicates poor network conditions or some othercondition that will effect network data usage or system performance,network daemon 406 can vote “yes” on the admission of the requestedattribute.

Thermal Daemon

In some implementations, a thermal daemon 110 application can beconfigured as an admission control voter. The thermal daemon 110 can beconfigured to use a voting API of sampling daemon 102 that allows thethermal daemon 110 to receive voting requests from sampling daemon 102and provide voting (e.g., yes, no) responses to sampling daemon 102. Forexample, the thermal daemon can receive a voting request from samplingdaemon 102 that includes an attribute and/or attribute value. Thethermal daemon 110 can indicate that sampling daemon 102 should notadmit or allow an event associated with an attribute or attribute valuewhen the thermal daemon 110 has detected a thermal event. For example,the thermal daemon 110 can monitor the temperature of the mobile device100 and report temperature values to sampling daemon 102 by generatingevents that include the “thermalLevel” attribute and correspondingtemperature value.

In some implementations, when thermal daemon 110 determines that thetemperature of mobile device 100 is above a threshold temperature value,thermal daemon 110 can prevent thermal daemon 102 from allowingattribute events that may increase the operating temperature of mobiledevice 100 further by returning a “no” value when sampling daemon 102sends a request to thermal daemon 110 to vote on an attribute (e.g.,“bundleId”) event.

In some implementations, sampling daemon 102 will only ask for a votefrom thermal daemon 110 when an abnormal thermal condition currentlyexists. For example, sampling daemon 102 can maintain a thermalcondition value (e.g., true, false) that indicates whether the mobiledevice 100 is operating at normal thermal conditions. If the currentthermal condition of mobile device 100 is normal, then the thermalcondition value can be true, for example. If the current thermalcondition of mobile device 100 is abnormal (e.g., too hot, above athreshold temperature), then the thermal condition value can be false.Initially, the thermal condition value can be set to true (e.g., normaloperating temperatures). Upon detecting that operating temperatures haverisen above a threshold temperature, thermal daemon 110 can sendsampling daemon 102 an updated value for the thermal condition valuethat indicates abnormal operating temperatures (e.g., false). Once themobile device 100 cools down to a temperature below the thresholdtemperature, thermal daemon 110 can update the thermal condition valueto indicate normal operating temperatures (e.g., true).

When sampling daemon 102 receives an admission control request for anattribute, sampling daemon 102 can check the thermal condition value todetermine whether to ask thermal daemon 110 to vote on admission(allowance) of the attribute event. If the thermal condition valueindicates normal operating temperatures (e.g., value is true), samplingdaemon 102 will interpret the thermal condition value as a “yes” votefrom thermal daemon 110.

If the thermal condition value indicates an abnormal operatingtemperature (e.g., value is false), sampling daemon 102 will send theattribute and/or attribute value to thermal daemon 110 to allow thethermal daemon 110 to vote on the specific attribute or attribute value.

In some implementations, thermal daemon 110 can determine how to vote(e.g., yes, no) on attributes and/or attribute values based on thecurrent thermal condition of the mobile device 100 and a peer forecastfor the attribute. For example, thermal daemon 110 can request a peerforecast for the attribute from sampling daemon 102. Thermal daemon 110can request a peer forecast for the current time by generating a windowspecification that includes the current time (e.g., +−1 hour, 2 hours,etc.) in the time period of interest. Thermal daemon 110 will receive apeer forecast from the sampling daemon 102 that indicates likelihoodscores for each value of the attribute that appears in the time periodof interest. For example, if thermal daemon 110 requests a peer forecastfor the “bundleId” attribute, thermal daemon 110 can receive a list of“bundleId” values (e.g., application identifiers) and associatedforecast (e.g., probability, likelihood) scores. For example, if, duringthe time period of interest, “bundleId” events having attribute values“mailapp,” “contacts,” “calendar,” “webbrowser,” “mailapp,”“webbrowser,” “mailapp” occur, then the relative likelihood (i.e.,score) of “mailapp” occurring is 0.43 (e.g., 3/7), the relativelikelihood of “webbrowser” occurring is 0.29 (e.g., 2/7) and therelative likelihoods for “contacts” or “calendar” occurring is 0.14(e.g., 1/7). In some implementations, thermal daemon 110 can order thelist of attribute values according to score (e.g., highest scores attop, lowest scores at bottom). For example, the ordered list for theabove “bundleId” attribute values from top to bottom is: “mailapp;”“webbrowser;” “contacts;” and “calendar”.

In some implementations, thermal daemon 110 can determine when to voteyes on an attribute value based on where an attribute value is in theordered list. For example, if the attribute value under consideration bythermal daemon 110 is not in the peer forecast list received fromsampling daemon 102, then the attribute value will receive a ‘no’ votefrom thermal daemon 110. If the attribute value is in the peer forecastlist and is below a threshold level (e.g., index) in the list (e.g., inthe bottom 25% of attributes based on scores), then thermal daemon 110will vote ‘no’ on the attribute. If the attribute value is in the peerforecast list and is above a threshold level in the list (e.g., in thetop 75% of attributes based on scores), then thermal daemon 110 willvote ‘yes’ on the attribute. Once the vote is determined, thermal daemon110 will return the ‘yes’ (e.g., true) or ‘no’ (e.g., false) vote tosampling daemon 102.

In some implementations, thermal daemon 110 can be configured with amaximum threshold level to avoid voting ‘no’ on all attribute values(e.g., so that some attribute events will occur). The maximum thresholdlevel can be 50% (e.g., top 50% get a ‘yes’ vote, bottom 50% get a ‘no’vote) of attribute values in the ordered peer forecast list. Thermaldaemon 110 can, therefore, adjust the threshold level that separatesattribute values that will receive a ‘yes’ vote from attribute valuesthat will receive a ‘no’ vote from the 0% to 50% of the attribute valueswith the lowest scores.

In some implementations, the threshold level for determining ‘yes’ or‘no’ votes can be proportional to the thermal level (e.g., temperature)of mobile device 100. For example, thermal daemon 110 can be configuredwith a maximum operating thermal level (L_(h)) and a normal operatinglevel (L_(n)). Thermal daemon 110 can determine the current operatingthermal level (L_(c)) and determine what percentile of the thermal range(e.g., L_(h)−L_(n)) the mobile device 100 is currently operating at(e.g., L_(c)−L_(n)/L_(h)−L_(n)=%). Thermal daemon 110 can use thecalculated percentile to determine what portion of the 0-50% attributevalues should receive a ‘no’ vote. For example, if the current operatingthermal level is calculated to be 65% of the thermal range, then thebottom 32.5% of attribute values by peer forecast score will receive a‘no’ vote from thermal daemon 110. Thus, the least important attributevalues will receive a ‘no’ vote while the most important attributevalues will receive a ‘yes’ vote. Referring back to the “bundleId”example above, if the ordered list for the above “bundleId” attributevalues from top to bottom is: “mailapp;” “webbrowser;” “contacts;” and“calendar”, then “calendar” would receive a ‘no’ vote and “mailapp,”“webbrowser,” and “contacts” would receive a ‘yes’ vote (e.g.,“mailapp,” “webbrowser,” and “contacts” being the most usedapplications). For example, if application manager 106 has made anadmission control request for the “bundleId” attribute to determinewhich applications to launch, then “mailapp,” “webbrowser,” and“contacts” applications would be launched and “calendar” applicationwould not be launched.

As another example, thermal daemon 110 can be asked to vote on the“mailapp.mailbox” attribute. A peer forecast can be generated for“mailapp.mailbox” attribute values that produce an ordered list of mailfolders that indicate the most frequently accessed folder to the leastfrequently accessed folder (e.g., “inbox;” “personal;” “work;” “family;”“spam;” and “trash”). If the bottom 32.5% of attribute values are toreceive a ‘no’ vote, then “spam” and “trash” will receive a ‘no’ vote.For example, if the “mailbox” application made the admission controlrequest for the “mailapp.mailbox” attribute to determine which foldersto fetch email for, then the “mailapp” application will fetch email forthe “inbox,” “personal,” “work,” and “family” folders and not fetchemail for the “spam” and “trash” folders. In some implementations,attributes or attribute values that have received a ‘no’ vote fromthermal daemon 110 can be notified when the thermal condition valuemaintained by sampling daemon 102 is reset to indicate normal operatingtemperatures (e.g., true value). For example, sampling daemon 102 canstore data that identifies clients, attributes and attribute values thathave received a ‘no’ vote. Upon receiving an updated thermal conditionvalue (e.g., true) from thermal daemon 110, sampling daemon 102 can senda notification to the clients that received a ‘no’ vote to prompt theclient to attempt another admission control request for the previouslyrejected attribute or attribute value. In some implementations, clientscan resend an admission control request without prompting from samplingdaemon 102. For example, a client may have an internal timer that causesthe client to retry the admission control request after a period of timehas elapsed.

Activity Monitor

In some implementations, an activity monitor application 408 can beconfigured as an admission control voter. The activity monitor 408 canbe configured to use a voting API of sampling daemon 102 that allows theactivity monitor 408 to receive voting requests from sampling daemon 102and provide voting (e.g., yes, no) responses to sampling daemon 102. Forexample, the activity monitor 408 can receive a voting request fromsampling daemon 102 that includes an attribute and/or attribute value.The activity monitor 408 can indicate that sampling daemon 102 shouldnot admit or allow an event associated with an attribute or attributevalue when mobile device 100 is using more than a threshold amount(e.g., 90%) of memory resources or CPU resources. For example, if mobiledevice 100 is already running many applications or processes that areusing most of the memory resources or CPU resources of the mobile device100, launching additional applications in the background will likelyreduce the performance of the mobile device 100 by using up remainingmemory resources. Thus, when the activity monitor 408 determines thatmemory or CPU usage exceeds a threshold value (e.g., 75%), activitymonitor 408 can prevent application manager 106 from launchingadditional applications by returning a “no” value when sampling daemon102 sends a request to vote on a “bundleId” attribute event. If theactivity monitor 408 determines that the memory and/or CPU resources ofmobile device 100 are below the threshold usage amount, the activitymonitor 408 can return a “yes” value in response to the vote requestfrom sampling daemon 102.

Launching a Background Fetch Application

In some implementations, when application manager 106 makes an admissioncontrol request to sampling daemon 102 and receives a “yes” reply,application manager 106 can invoke or launch the identified application(e.g., as identified by the “bundleId” attribute value, application 108)in the background of the operating environment of mobile device 100. Forexample, the application 108 can be launched in the background such thatit is not apparent to the user that application 108 was launched. Theapplication 108 can then communicate over a network (e.g., the internet)with content server 404 to download updated content for display to theuser. Thus, when the user subsequently selects application 108 (e.g.,brings the application to the foreground), the user will be presentedwith current and up-to-date content without having to wait forapplication 108 to download the content from server 404 and refresh theapplication's user interfaces.

In some implementations, application manager 106 can be configured tolaunch background fetch enabled applications when the mobile device 100is charging and connected to Wi-Fi. For example, sampling daemon 102 candetermine when mobile device 100 is connected to an external powersource (e.g., based on “cablePlugin” attribute events) and connected tothe network (e.g., internet) over Wi-Fi (e.g., based on received events)and send a signal to application manager 106 to cause applicationmanager 106 to launch fetch enabled applications that have been usedwithin a previous amount of time (e.g., seven days).

Example Background Fetch Processes

FIG. 6 is a flow diagram of an example process 600 for predictivelylaunching applications to perform background updates. For example,process 600 can be performed by application manager 106 and samplingdaemon 102 to determine when to launch background applicationsconfigured to fetch data updates from network resources, such as contentserver 404 of FIG. 4. Additional description related to the steps ofprocess 600 can be found with reference to FIG. 4 and FIG. 5 above.

At step 602, application manager 106 can receive an applicationinvocation forecast from sampling daemon 102. For example, applicationmanager 106 can be launched during startup of mobile device 100. Duringits initialization, application manager 106 can request a forecast ofapplications likely to be invoked by a user of the mobile device 100over the next 24-hour period. For example, application manager 106 canrequest a temporal forecast for attribute “bundleId.” This forecast canindicate when to launch applications. For example, a 24-hour period canbe divided into 15-minute blocks and each 15-minute block can beassociated with a probability that the user will invoke an applicationduring the 15-minute block. The forecast returned to application manager106 can identify up to 64 15-minute blocks of time when the user islikely to invoke an application.

At step 604, application manager 106 can set timers based on theapplication launch forecast. For example, application manager 106 canset a timer or alarm for each of the 15 minute blocks identified in theapplication launch forecast returned to the application manager 106 bysampling daemon 102.

At step 606, application manager 106 can request sampling daemon 102identify what applications to launch. For example, when a timer expiresor alarm goes off, application manager can wake, if sleeping orsuspended, and request from sampling daemon 102 a list of applicationsto launch for the current 15-minute block of time. Sampling daemon 102can return a list of applications that should be launched in thebackground on mobile device 100. For example, application manager 106can request a peer forecast for attribute “bundleId”. The peer forecastcan indicate which values of the “bundleId” attribute are most likely tobe reported (e.g., which applications are most likely to be invoked bythe user) in the current 15-minute timeslot.

At step 607, application manager 106 can send a request to samplingdaemon 102 asking if it is ok to launch an application. For example, foreach application identified by sampling daemon 102 in response to the“bundleId” peer forecast request, application manager 106 can asksampling daemon 102 whether it is ok to launch the application. Forexample, application manager 106 can request that sampling daemon 102perform admission control on a particular value of the “bundleId”attribute that corresponds to an application that application manager106 is attempting to launch. Sampling daemon 102 can return “yes” fromthe admission control request if it is ok to launch the application,“no” if it is not ok to launch the application, or “never” if it isnever ok to launch the application.

At step 610, application manager 106 can launch an application. Forexample, if sampling daemon 102 returns an “ok” (e.g., ok, yes, true,etc.) response to the admission control request, application manager 106will launch the application as a background process of mobile device100. If sampling daemon 102 returns a “no” or “never” response to theadmission control request, application manager 106 will not launch theapplication.

At step 612, application manager 106 can transmit an application launchnotification to sampling daemon 102. For example, application manager106 can transmit a “bundleId” start event to sampling daemon 102 torecord the execution of the launched application.

At step 614, application manager 106 can detect that the launchedapplication has terminated. For example, application manager 106 candetermine when the launched application is no longer running on mobiledevice 100.

At step 616, application manager 106 can transmit an applicationtermination notification to sampling daemon 102. For example,application manager 106 can transmit a “bundleId” end event to samplingdaemon 102 to record the termination of the application.

FIG. 7 is a flow diagram of an example process 700 for determining whento launch applications on a mobile device 100. For example, process 700can be used to determine when to launch applications, what applicationsshould be launched and if it is ok to launch applications based onapplication use statistics (e.g., “bundleId” attribute event data), dataand energy budgets, and mobile device operating and environmentalconditions, as described above in detail with reference to FIG. 4

At step 702, sampling daemon 102 can receive an application launchforecast request from application manager 106. For example, applicationmanager 106 can request a temporal forecast for the “bundleId” attributefor the next 24 hours from sampling daemon 102. Once the 24-hour periodhas passed, application manager 106 can request a temporal forecast forthe “bundleId” attribute for the subsequent 24 hour period. For example,application manager 106 can request temporal forecast for the “bundleId”attribute every 24 hours.

At step 704, sampling daemon 102 can determine an application launchforecast. For example, the application launch forecast (e.g., temporalforecast for the “bundleId” attribute) can be used to predict whenuser-initiated application launches are likely to occur during a 24-hourperiod. The 24-hour period can be divided into 15-minute time blocks.For each 15-minute time block (e.g., there are 96 15-minute time blocksin a 24 hour period), sampling daemon 102 can use historical userinvocation statistics (e.g., “bundleId” start events) to determine aprobability that a user initiated application launch will occur in the15-minute time block, as described above with reference to FIG. 4.

At step 706, sampling daemon 102 can transmit the application launchforecast to application manager 106. For example, sampling daemon 102can select up to 64 15-minute blocks having the highest non-zeroprobability of a user initiated application launch. Each of the selected15-minute blocks can be identified by a start time for the 15-minuteblock (e.g., 12:45 pm). Sampling daemon 102 can send the list of15-minute block identifiers to application manager 106 as theapplication launch forecast (e.g., temporal forecast for the “bundleId”attribute).

At step 708, sampling daemon 102 can receive a request for whatapplications to launch at a current time. For example, applicationmanager 106 can send a request to sampling daemon 102 for samplingdaemon 102 to determine which applications should be launched at oraround the current time. For example, the request can be a request for apeer forecast for the “bundleId” attribute for the current 15-minutetimeslot.

At step 710, sampling daemon 102 can score applications for the currenttime based on historical event data. Sampling daemon 102 can determinewhich applications that the user is likely to launch in the near futurebased on historical user initiated application launch data (e.g.,“bundleId” attribute start event data) collected by sampling daemon 102.Sampling daemon 102 can utilize recent application launch data, dailyapplication launch data and/or weekly application launch data to scoreapplications based on the historical likelihood that the user willinvoke the application at or around the current time, as described abovewith reference to FIG. 4 and FIG. 5.

At step 712, sampling daemon 102 can transmit the applications andapplication scores to application manager 106. For example, samplingdaemon 102 can select a number (e.g., three) of applications (e.g.,“bundleId” attribute values) having the highest scores (e.g., highestprobability of being invoked by the user) to transmit to applicationmanager 106. Sampling daemon 102 can exclude applications that have beenlaunched within a previous period of time (e.g., the previous 5minutes). Sampling daemon 102 can transmit information that identifiesthe highest scored applications and their respective scores toapplication manager 106, as described above with reference to FIG. 4.

At step 714, sampling daemon 102 can receive a request from applicationmanager 106 to determine whether it is ok to launch an application. Forexample, sampling daemon 102 can receive an admission control requestthat identifies an application (e.g., “bundleId” value).

At step 716, sampling daemon 102 can determine that current mobiledevice conditions and budgets allow for an application launch. Forexample, in response to the admission control request, sampling daemon102 can check system-wide data and energy budgets, attribute budgets andvoter feedback to determine whether the application should be launchedas a background task on mobile device 100, as described in detail abovewith reference to FIG. 4.

At step 718, sampling daemon 102 can transmit a reply to applicationmanger 106 indicating that it is ok to launch the identifiedapplication. For example, if conditions are good for a backgroundapplication launch, sampling daemon 102 can return a “yes” value (e.g.,ok, yes, true, etc.) to application manager 106 in response to theadmission control request so that application manager 106 can launch theidentified application.

Short Term Trending

In some implementations, sampling daemon 102 can be configured to detectwhen attributes are trending. For example, a client application mayregister interest in a particular attribute with sampling daemon 102.When sampling daemon 102 detects that the particular attribute istrending, sampling daemon 102 can notify the client that the particularattribute is trending.

For example, application manager 106 can register interest in the“bundleId” attribute (or a particular value of the “bundleId”attribute). When sampling daemon 102 determines that the “bundleId”attribute (or value thereof) is trending, sampling daemon 102 can notifyapplication manager 106 of the trend so that application manager 106 canpredictively launch the trending application in the background on mobiledevice 100. For example, an application is trending if the applicationis being repeatedly invoked by a user of mobile device 100. In somecases, the trending application is a new application or, prior to thetrend, a rarely used application that may not be included in the“bundleId” attribute peer forecast described above. Thus, the trendingapplication may not be kept up to date using the application launchforecasting methods described above.

The purpose of attribute trend detection is to detect attributes (e.g.,attribute events) that are being reported repeatedly to sampling daemon102 and to determine an approximate cadence (e.g., periodicity) withwhich the attributes are being launched, erring on reporting a smallercadence. Attributes that are being reported repeatedly to the samplingdaemon 102 are said to be “trending.” The determined cadence can then beused by sampling daemon 102 clients to perform functions or operationsin anticipation of the next event associated with the trendingattribute.

For example, the determined cadence can be used by application manager106 to set timers that will trigger the application manager 106 tolaunch the trending applications in the background so that theapplications will be updated when the user invokes the applications, asdescribed above. For example, if the cadence is 5 minutes for anapplication, application manager 106 can set a timer that will expireevery 4 minutes and cause application manager 106 to launch theapplication so that the application can receive updated content andupdate the application's interfaces before being invoked again by theuser.

In some implementations, the trend detection mechanisms described hereincan be used to detect other system event trends beyond applicationlaunches, such as repeated software or network notifications,application crashes, etc. For example, clients can register interest inany attribute or attribute value and can receive notifications when theattributes of interest are trending.

In some implementations, sampling daemon 102 can maintain a trendingtable that can be used to track the behavior of a number of attributes.The trending table can include an attribute value identification field(ATTID), a state field (STATE), a last launch timestamp (LLT), aninter-launch cadence (ILC) that indicates the amount of time betweenlaunches, and a confidence field (C).

FIG. 8 is a flow diagram 800 illustrating state transitions for an entry(e.g., application) in the trending table. Initially at step 802, thetrending table can include empty entries (e.g., records) where theATTID, LLT, ILC and C fields are empty (e.g., N/A) and the STATE is setto “invalid” (I). When an attribute event is reported at time t, thetrending table is scanned for an available entry (e.g., an entry instate I). Among the possible invalid entries, several methods can beused for selecting an entry to use. For example, a random invalid entrycan be selected. Alternatively, an invalid entry can be selected suchthat all the empty entries in the trending table are kept in consecutiveorder. If no invalid entry exists, the oldest entry (or a random entry)in transient (T) state can be selected to track the newly launchedapplication. If no I or T state entries exist, the oldest new (N) stateentry can be selected to track the newly reported attribute event.

At step 804, once the trending table entry is selected, the STATE fieldof the selected entry for tracking the newly reported attribute eventcan be set to new (N), the ATTID can be set to the attribute value ofthe newly reported attribute, the LLT field can be set to the currenttime t (e.g., wall clock time) and the ILC and C fields are set topredefined minimum values ILC_MIN (e.g., 1 minute) and C_MIN (e.g.,zero).

At step 806, on the next report of the same attribute event at time t′,the entry in the table for the attribute is found, if it still existsand has not been evicted (e.g., selected to track another attribute).The STATE of the entry is set to transient (T), the ILC is set to thedifference between the LLT and the current system time (e.g., t′−t ort′−LLT), and the C field is incremented (e.g., by predefined valueC_DELTA). Alternatively, the ILC field can be set to some other functionof its old and new values, such as the running average.

At step 808, on the next report of the same attribute event at time t″,the entry in the table for the attribute is found, if it still existsand has not been evicted (e.g., selected to track another attribute).The STATE of the entry can remain set to transient (T), the ILC is setto the difference between the LLT and the current (e.g., wall) clocktime (e.g., t″−t′ or t″−LLT), and the C field is incremented again(e.g., by predefined value C_DELTA).

At step 810, if, after several reports of the attribute event, the Cvalue of the trending table entry reaches (e.g., equals) a thresholdvalue (e.g., C_HIGHTHRESHOLD), at step 811, the state of the attributeentry can be changed to STATE=A. If, at step 810, the C value of thetrending table entry does not reach the threshold value (e.g.,C_HIGHTHRESHOLD), the values of the entry can be updated according tostep 808.

Whenever the attribute event is reported while in state “A”, if the timebetween the last report and the time of the current report is withinsome amount of time (e.g., ILC_EPSILON=5 minutes), then the attributeentry's confidence (C) field is incremented until it reaches apredefined maximum value (e.g., C_MAX). When an attribute entry in thetrending table is in the active (A) state, the entry's ILC value can beused as an estimation of the rate of launch (e.g., cadence) and theentry's ATTID can be used to identify the trending attribute value.

In some implementations, sampling daemon 102 can send the attributevalue (ATTID) and cadence value (ILC) to a client so that the client canperform some action or function in anticipation of the next eventassociated with the attribute value. For example, the attribute valueand cadence value can be sent to application manager 106 so thatapplication manager 106 can launch the identified application (e.g.,ATTID, “bundleId” attribute value) in the background in anticipation ofa user invocation of the application so that the application can receiveupdated content prior the user launching the application, as describedabove. For example, application manager 106 can start a timer based onthe cadence value that will wake the application manager 106 to launchthe application in anticipation of a user invoking the application.

In some implementations, sampling daemon 102 can notify clients of theanticipated next occurrence of an attribute event based on a detectedattribute trend. For example, sampling daemon 102 can send applicationmanager 106 a signal or notification indicating that a trendingapplication should be launched by application manager 106. Applicationmanager 106 can register interest in an application by sending samplingdaemon 102 an application identifier (e.g., “bundleId” attribute value).Sampling daemon 102 can monitor the application for user invocation(e.g., based on reported “bundleId” start events) to determine whetherthe application is trending, as described above. If the application istrending, sampling daemon 102 can determine the cadence of invocation,as described above, and send a notification or signal to applicationmanager 106 at a time determined based on the cadence. For example, ifthe cadence is four minutes, sampling daemon 102 can send a signal toapplication manager 106 every 3 minutes (e.g., some time period beforethe next occurrence of the event) to cause application manager 106 tolaunch the application. If the cadence changes to six minutes, samplingdaemon 102 can detect the cadence change and adjust when applicationmanager 106 is signaled. For example, sampling daemon 102 can signalapplication manager 106 to launch the application every 5 minutesinstead of every 3 minutes to adjust for the decreased cadence (e.g.,increased time period between invocations).

At each inspection of the attribute trending table for any reason (e.g.,adding a new entry, updating an existing entry, etc.), all entries inSTATE=T or STATE=A whose time since last launch is greater than theirILC by ILC_EPSILON will have their C values decremented. Any entry whoseC value at that point falls below a minimum threshold value (e.g.,C_LOWTHRESHOLD) is demoted. An entry can be demoted from state A tostate T or from state T to state I, for example.

In some implementations, the trend detection mechanism described abovecan be used to detect trending events other than application invocationsor launches. For example, the trend detection method and trending tabledescribed above can be used to detect and track any recurring event(e.g., any attribute event) on mobile device 100. A trending event caninclude screen touches, network connections, application failures, theoccurrence of network intrusions and/or any other event that can bereported or signaled to sampling daemon 102.

Push Notifications

FIG. 9 is a block diagram 900 illustrating a system for providing pushnotifications to a mobile device 100. In some implementations, mobiledevice 100 can be configured to receive push notifications. For example,a push notification can be a message that is initiated by a pushprovider 902 and sent to a push service daemon 904 running on mobiledevice 100 through push notification server 906.

In some implementations, push provider 902 can receive authorization tosend push notifications to mobile device 100 through a userauthorization request presented to a user of mobile device 100 byapplication 908. For example, push provider 902 can be a server owned,operated and/or maintained by the same vendor that created (e.g.,programmed, developed) application 908. Push provider 902 can receiveauthorization from a user to send push notifications to mobile device100 (e.g., push service daemon 904) when application 908 presents a userinterface on mobile device 100 requesting authorization for pushprovider 902 to send push notifications to mobile device 100 and theuser indicates that push notifications are authorized. For example, theuser can select a button on the user interface presented by application908 to indicate that push notifications are authorized for the pushprovider 902 and/or application 908. Push provider 902 can then receivea device token that identifies mobile device 100 and that can be used toroute push notifications to mobile device 100. For example, pushnotification server 906 can receive a device token with a pushnotification and use the device token to determine which mobile device100 should receive the push notification.

In some implementations, mobile device 100 can send informationidentifying authorized push applications to push notification server906. For example, mobile device 100 can send a message 926 containingpush notification filters 914 and the device token for mobile device 100to push notification server 906. Push notification server 906 can storea mapping of device tokens (e.g., identifier for mobile device 100) topush filters 914 for each mobile device serviced by push notificationserver 906. Push filters 914 can include information identifyingapplications that have received authorization to receive pushnotifications on mobile device 100, for example.

In some implementations, push filters 914 can be used by pushnotification server 906 to filter out (e.g., prevent sending) pushnotifications to applications that have not been authorized by a user ofmobile device 100. Each push notification sent by push provider 902 topush notification server 906 can include information (e.g., anidentifier) that identifies the application 908 associated with pushprovider 902 and the mobile device 100 (e.g., device token).

When notification server 906 receives a push notification, notificationserver 906 can use the mobile device identification information (e.g.,device token) to determine which push filters 914 to apply to thereceived push notification. Notification server 906 can compareapplication identification information in the push notification to thepush filters 914 for the identified mobile device to determine if theapplication associated with push provider 902 and identified in the pushnotification is identified in the push filter 914. If the applicationassociated with the push notification is identified in the push filters914, then the notification server 906 can transmit the push notificationreceived from push provider 902 to mobile device 100. If the applicationidentified in the push notification is not identified in the pushfilters 914, then the notification server will not transmit the pushnotification received from push provider 902 to mobile device 100 andcan delete the push notification.

Non-Waking Push Notifications

In some implementations, notification server 906 can be configured toprocess high priority push notifications and low priority pushnotifications. For example, push provider 902 can send a high prioritypush notification 910 and/or a low priority push notification 912 topush notification server 906. Push provider 902 can identify a pushnotification as high or low priority by specifying the priority of thepush notification in data contained within the push notification sent topush notification server 906 and mobile device 100, for example.

In some implementations, push notification server 906 can process lowpriority push notification 912 differently than high priority pushnotification 910. For example, push notification server 906 can beconfigured to compare application identification information containedin high priority push 910 with authorized application identificationinformation in push filters 914 to determine if high priority pushnotification 910 can be transmitted to mobile device 100. If theapplication identification information in high priority pushnotification 910 matches an authorized application identifier in pushfilters 914, then push notification server 906 can transmit the highpriority push notification to mobile device 100. If the applicationidentification information in high priority push notification 910 doesnot match an authorized application identifier in push filters 914, thenpush notification server 906 will not transmit the high priority pushnotification to mobile device 100.

In some implementations, push notification server 906 can be configuredto delay delivery of low priority push notifications. For example, whenmobile device 100 receives a push notification from push notificationserver 906, the receipt of the push notification causes mobile device100 to wake up (e.g., if in a sleep or low power state). When mobiledevice 100 wakes, mobile device 100 will turn on various subsystems andprocessors that can drain the battery, use cellular data, cause themobile device 100 to heat up or otherwise effect the mobile device 100.By preventing or delaying the delivery of low priority pushnotifications to mobile device 100, mobile device 100 can conservenetwork (e.g., cellular data) and system (e.g., battery) resources, forexample.

In some implementations, push notification filters 914 can include awake list 916 and a no wake list 918. The wake list 916 can identifyapplications for which low priority push notifications should bedelivered to mobile device 100. In some implementations, when anapplication is authorized to receive push notifications at mobile device100, the application identification information is added to the wakelist 914 by default. The no wake list 918 can identify authorizedapplications for which low priority push notifications should bedelayed. The specific mechanism for populating no wake list 918 and/ormanipulating wake list 916 and no wake list 918 is described in detailbelow when describing push notification initiated background updates. Insome implementations, high priority push notifications will not bedelayed at the push notification server 906 and will be delivered tomobile device 100 as long as the application identified in the highpriority push notification is identified in push filters 914 (e.g., wakelist 914 and/or no wake list 918).

In some implementations, when push notification server 906 receives alow priority push notification 912, push notification server 906 cancompare the application identifier in low priority push notification 912to wake list 916 and/or no wake list 918. For example, if theapplication identification information in the low priority pushnotification 912 matches an authorized application identifier in thewake list 916, the low priority push notification 912 will be deliveredto the mobile device 100 in a notification message 920.

In some implementations, delivery of low priority push notificationsassociated with applications identified in the no wake list 918 can bedelayed. For example, if an application identified in low priority pushnotification 912 is also identified in no wake list 918, then lowpriority push notification 912 can be stored in push notification datastore 922 and not immediately delivered to mobile device 100. In someimplementations, if the mobile device 100 identified by a pushnotification (high or low priority) is not currently connected to pushnotification server 906, the push notification for the disconnectedmobile device 100 can be stored in push notification data store 922 forlater delivery to mobile device 100.

In some implementations, push notifications stored in push data store922 will remain in push data store 922 until the application identifierassociated with a stored push notification is moved from the no wakelist 918 to wake list 916 or until a network connection is establishedbetween push notification server 906 and mobile device 100. For example,a network connection between push notification server 906 and mobiledevice 100 can be established when another (high or low priority) pushnotification is delivered to mobile device 100 or when mobile device 100sends other transmissions 924 (e.g., status message, heartbeat message,keep alive message, etc.) to push notification server 906. For example,mobile device 100 can send a message 924 to push notification server 905indicating that the mobile device 100 will be active for a period oftime (e.g., 5 minutes) and push notification server 906 can send allreceived push notifications to mobile device 100 during the specifiedactive period of time. In some implementations, when a networkconnection is established between mobile device 100 and pushnotification server 906 all push notifications stored in pushnotification store 922 will be delivered to mobile device 100. Forexample, push notifications stored in push notification data store 922can be transmitted through connections created by other transmissionsbetween mobile device 100 an push notification server 906.

In some implementations, mobile device 100 can establish two differentcommunication channels with push notification server 906. For example,the two communication channels can be established simultaneously or atdifferent times. The mobile device 100 can have a cellular dataconnection and/or a Wi-Fi connection to push notification server 906,for example. In some implementations, mobile device 100 can generate andtransmit to push notification server 906 different push filters 914 foreach communication channel. For example, a cellular data connection canbe associated with first set of push filters 914 for determining when tosend high and low priority push notifications across the cellular dataconnection. A Wi-Fi data connection can be associated with a second setof push filters 914 that are the same or different than the cellulardata push filters for determining when to send high and low prioritypush notifications across the Wi-Fi data connection. When pushnotification server 906 receives a push notification, push notificationserver can compare the application identified in the push notificationto the push notification filters for the communication channel (e.g.,Wi-Fi, cellular) that the push notification server 906 will use totransmit the push notification to the mobile device 100.

Push Initiated Background Updates

In some implementations, receipt of push notifications by mobile device100 can trigger a background update of applications on the mobile device100. For example, when mobile device 100 (e.g., push service daemon 904)receives a push notification message 920 from push notification server906, push service daemon 904 can compare the application identifier inthe push notification message 920 to push filters 928 stored on mobiledevice 100 to determine if the push notification message 920 wasproperly delivered or should have been filtered (e.g., not delivered) bypush notification server 906. For example, push filters 928, wake list930 and no wake list 932 can correspond to push filters 914, wake list916 and no wake list 918, respectively. In some implementations, if pushservice daemon 904 determines that the push notification message 920should not have been delivered to mobile device 100, the pushnotification message 902 will be deleted.

Low Priority Push Notifications

In some implementations, the push notification message 920 received bymobile device 100 can include a low priority push notification. Forexample, the low priority push notification can indicate that contentupdates are available for the application associated with the pushnotification. Thus, when the low priority push notification causes alaunch of an application 908, the application 908 can download updatedcontent from one or more network resources (e.g., push provider 902).

In some implementations, when push service daemon 904 receives a lowpriority push notification associated with an application (e.g.,application 908) on mobile device 100, push service daemon 904 can asksampling daemon 102 if it is ok to launch the application associatedwith the received low priority push notification. For example, pushservice daemon 904 can request that sampling daemon 102 performadmission control by sending sampling daemon 102 an identifier for theapplication (e.g., “bundleId” attribute value) associated with thereceived low priority push notification. Sampling daemon 102 can performadmission control by checking data budgets, energy budgets, attributebudgets and voter feedback, as described above with reference to FIG. 4.Sampling daemon 102 can return to push service daemon 904 a valueindicating whether it is ok to launch the application identified by thelow priority push notification based on the outcome of the admissioncontrol process.

In some implementations, if the value returned from the admissioncontrol request indicates “yes” it is ok to launch the application, pushservice daemon 904 will send the low priority push notification toapplication manager 106 and application manager 106 can invoke theapplication (e.g., application 908). Application 908 can thencommunicate with push provider 902 over the network (e.g., the internet)to receive updated content from push provider 902.

In some implementations, if the value returned from the admissioncontrol request indicates “no” it is not ok to launch the application,push service daemon 904 will store the low priority push notification inpush notification data store 934. For example, when storing a lowpriority push notification, push service daemon 904 will only store thelast push notification received for the application identified in thepush notification.

In some implementations, when sampling daemon 102 indicates that pushservice daemon 904 should not launch an application right now (e.g., theadmission control reply is “no”), push service daemon 904 can move theapplication identifier for the application from wake list 930 to no wakelist 932. For example, if sampling daemon 102 determines that thebudgets, and/or conditions of the mobile device do not allow forlaunching the application, allowing the push notification server 906 towake mobile device 100 for additional low priority push notificationsassociated with the application will just further consume the data andenergy budgets of the mobile device 100 or make environmental conditionsworse (e.g., cause the device to heat up). Thus, by moving theapplication identifier into the no wake list 932 and sending a message926 to push notification server 906 that includes the updated filters928 (e.g., wake list 930 and no wake list 932), notification server 906can update its own push filters 914, wake list 916 and no wake list 918to reflect the changes to push filters 928 and to prevent additional lowpriority push notifications for the application from being delivered tomobile device 100.

In some implementations, if the value returned from the admissioncontrol request indicates that it is “never” ok to launch theapplication, push service daemon 904 will delete the low priority pushnotification and remove the application identifier associated with thepush notification from push filters 928. The updated push filters can betransmitted to push notification server 906 and push filters 914 on pushnotification server 906 can be updated to prevent push notificationserver 906 from sending any more push notifications associated with theapplication identifier.

In some implementations, sampling daemon 102 can transmit a “stop”signal to push service daemon 904 to temporarily prevent future lowpriority push notifications from being sent from push notificationserver 906 to mobile device 100. For example, sampling daemon 102 cansend a stop signal to push service daemon 904 when sampling daemon 102determines the data budget is exhausted for the current hour, the energybudget is exhausted for the current hour, the system is experiencing athermal event (e.g., mobile device 100 is too hot), the mobile device100 has a poor cellular connection and the mobile device 100 is notconnected to Wi-Fi and/or that the mobile device 100 is connected to avoice call and not connected to Wi-Fi. When push service daemon 904receives a stop signal, push service daemon 904 can move the applicationidentifiers in wake list 930 to no wake list 932 and transmit theupdated push filters 928 to push notification server 906 to update pushfilters 914. Thus, push notification server 906 will temporarily preventfuture low priority push notifications from waking mobile device 100 andimpacting the budgets, limits and operating conditions of mobile device100.

In some implementations, sampling daemon 102 can transmit a retry signalto push service daemon 904. For example, sampling daemon 102 can monitorthe status of the budgets, network connections, limits and deviceconditions and will send a retry message to push service daemon 904 whenthe push data budget is not exhausted, when the energy budget is notexhausted, when the mobile device 100 is not experiencing a thermalevent, when the mobile device 100 has a good quality cellular connectionor is connected to Wi-Fi, when mobile device 100 is not connected to avoice call and when the launch rate limits have been reset. Once thepush service daemon 904 receives the retry signal, push service daemon904 will send an admission control request to sampling daemon 102 foreach push notification in push notification data store 934 to determineif it is ok to launch each application (e.g., “bundleId” attributevalue) associated with the stored push notifications.

If sampling daemon 102 returns a “yes” from the admission controlrequest, push service daemon 904 can send the push notification toapplication manager 106 and application manager 106 can launch theapplication associated with the push notification as a backgroundprocess on mobile device 100, as described above. Once the applicationis launched, the application can download content or data updates andupdate the applications user interfaces based on the downloaded data.Application manager 106 will not ask sampling daemon 102 if it is ok tolaunch an application associated with a low priority push notification.

High Priority Push Notifications

In some implementations, the push notification message 920 received bymobile device 100 can include a high priority push notification. Forexample, the high priority push notification can indicate that contentupdates are available for the application associated with the pushnotification. Thus, when the high priority push notification causes aninvocation of an application, the application can download updatedcontent from one or more network resources. In some implementations,when a high priority push notification is received by push servicedaemon 904, push service daemon 904 will send the high priority pushnotification to application manager 106 without making an admissioncontrol request to sampling daemon 102.

In some implementations, when application manager 106 receives a pushnotification associated with an application, application manager 106will make an admission control request to sampling daemon 102. Inresponse to the admission control request, sampling daemon 102 can replywith “yes,” “no,” or “never” responses as described above. Whenapplication manager 106 receives a “yes” reply to the admission controlrequest, application manager 106 can launch the application associatedwith the received high priority push notification as a backgroundprocess on mobile device 100.

In some implementations, when application manager 106 receives a “no”reply to an admission control request, application manager 106 can storethe high priority push notification in high priority push notificationstore 936. When application manager 106 receives a “never” response,application manager 106 can delete the high priority push notificationand delete any push notifications stored in push notification data store936 for the application associated with the push notification.

In some implementations, sampling daemon 102 can send an “ok to retry”signal to application manager 106. For example, when application manager106 receives an “ok to retry” message from sampling daemon 102,application manager 106 can make an admission control request for theapplications associated with each high priority push notification inhigh priority push notification data store 936 and launch the respectiveapplications as background processes when a “yes” reply is received inresponse to the admission control request.

Delaying Display of Push Notifications

In some implementations, high priority push notifications can cause agraphical user interface to be displayed on mobile device 100. Forexample, receipt of a high priority push notification can cause abanner, balloon or other graphical object to be displayed on a graphicaluser interface of mobile device 100. The graphical object can includeinformation indicating the subject matter or content of the receivedpush notification, for example.

In some implementations, when application manager 106 receives a highpriority push notification, application manager 106 can cause thenotification to be displayed on a graphical user interface of the mobiledevice 100. However, when the high priority push notification indicatesthat there are data updates to be downloaded to the applicationassociated with the high priority push notification, the application canbe launched in the background of mobile device 100 before the pushnotification is displayed. For example, application manager 106 can beconfigured with an amount of time (e.g., 30 seconds) to delay betweenlaunching an application associated with the high priority pushnotification and displaying the graphical object (e.g., banner) thatannounces the push notification to the user. The delay can allow theapplication enough time to download content updates and update theapplication's user interfaces before being invoked by the user, forexample. Thus, when the user provides input to the graphical object orotherwise invokes the application associated with the high priority pushnotification, the application's user interfaces will be up to date andthe user will not be forced to wait for updates to the application. Insome implementations, if application manager 106 is unable to launch theapplication associated with the high priority push notification, themobile device 100 will display the graphical object (e.g., banner) tonotify the user that the high priority push notification was received.

Example Push Notification Processes

FIG. 10 is a flow diagram of an example process 1000 for performingnon-waking pushes at a push notification server 906. At step 1002, pushnotification server 906 can receive a push notification. For example,push notification server 906 can receive a push notification from a pushnotification provider 902 (e.g., a server operated by an applicationvendor).

At step 1004, push notification server 906 can determine that the pushnotification is a low priority push notification. For example, the pushnotification provider can include data in the push notification thatspecifies the priority of the push notification. Push notificationserver 906 can analyze the contents of the push notification todetermine the priority of the push notification.

At step 1006, push notification server 906 can compare the pushnotification to a push notification filter. For example, the pushnotification can identify an application installed or configured onmobile device 100 to which the low priority push notification isdirected. The push notification can include an application identifier(e.g., a “bundleId” attribute value), for example. Push notificationserver 906 can compare the application identifier in the pushnotification to application identifiers in the push notificationfilter's no wake list 918.

At step 1008, push notification server 906 can determine that the lowpriority push notification should be stored. For example, if theapplication identifier from the low priority push notification is in thepush notification filter's no wake list 918, the push notificationserver 906 can determine that the low priority push should be stored inpush notification data store 922.

At step 1010, based on the determination at step 1008, the low prioritypush notification will be stored in a database or data store 922 of thepush notification server 906 and not immediately sent to the mobiledevice 100.

At step 1012, push notification server 906 can determine that a networkconnection to mobile device 100 has been established. For example, pushnotification server 906 can create a network connection to mobile device100 to deliver another high or low priority push. Mobile device 100 canestablish a network connection to push notification server 906 to sendnotification filter changes, periodic status updates, keep alivemessages or other messages to push notification server 906.

At step 1014, push notification server 906 can send the stored pushnotifications in response to determining that a network connection tomobile device 100 has been established. For example, push notificationserver 906 can send the low priority push notifications stored at thepush notification server 906 to mobile device 100.

FIG. 11 is a flow diagram of an example process 1100 for performingbackground updating of an application in response to a low priority pushnotification. At step 1102, mobile device 100 can receive a low prioritypush notification from push notification server 906.

At step 1104, mobile device 100 can determine if it is ok to launch anapplication associated with the low priority push notification. Forexample, the application can be launched as a background process onmobile device 100. Mobile device 100 can determine whether it is ok tolaunch the application using the admission control process describedabove. For example, mobile device 100 (e.g., sampling daemon 102) candetermine whether it is ok to launch the application based on data,energy and/or attribute budgets determined for the mobile device 100.Mobile device 100 can determine whether it is ok to launch theapplication based on conditions of the mobile device, and/or thecondition of the mobile device's network connections based on responsesfrom various voters. The details for determining whether it is ok tolaunch an application (e.g., admission control) are described in greaterdetail with reference to FIG. 4 above.

At step 1106, mobile device 100 can store the low priority pushnotification when device conditions, budgets, limits and other dataindicate that it is not ok to launch the application. For example,mobile device 100 can store the low priority push notifications in adatabase or other data store on mobile device 100.

At step 1108, mobile device 100 can update its push notification filtersin response to determining that it is not ok to launch a backgroundapplication. For example, mobile device 100 can move the applicationassociated with the low priority push notification to the no wake listof the push notification filters on mobile device 100.

At step 1110, mobile device 100 can transmit the updated notificationfilters to push notification server 906. Push notification server 906can update its own push notification filters based on the filtersreceived from mobile device 100 to determine when to transmit and whento not transmit low priority push notifications to mobile device 100.

At step 1112, mobile device 100 can determine that it is ok to retrylaunching applications associated with low priority push notifications.For example, mobile device 100 can determine that the budgets, limitsand device conditions, as described above, allow for launchingadditional background applications on the mobile device 100.

At step 1114, mobile device 100 can determine whether it is ok to launcha particular application associated with a stored low priority pushnotification. For example, sampling daemon 102 of mobile device 100 canperform admission control to determine that the budgets configured onmobile device 100 have been reset or replenished for the current timeand that the environmental conditions of the mobile device 100 andnetwork connections are good enough to launch the particular backgroundapplication.

At step 1116, mobile device 100 can launch the particular applicationwhen the mobile device 100 determines that it is ok to launch theapplication. For example, the particular application can be launched asa background process to download new content and update the userinterfaces of the application before a user invokes the application.This process will allow a user to invoke an application and not have towait for content updates to be downloaded and for user interfaces of theapplication to be refreshed.

FIG. 12 is a flow diagram of an example process 1200 for performingbackground updating of an application in response to a high prioritypush notification. At step 1202, mobile device 100 can receive a highpriority push notification.

At step 1204, mobile device 100 can determine if it is ok to launch anapplication associated with the high priority push notification. Forexample, sampling daemon 102 of mobile device 100 can perform admissioncontrol to determine whether it is ok to launch the application based onbudgets and environmental conditions of the mobile device 100 (e.g.,device conditions, network conditions, etc.).

At step 1206, mobile device 100 can store the high priority pushnotification when it is not ok to launch (e.g., admission controlreturns “no”) the application associated with the high priority pushnotification. For example, mobile device 100 can store the high prioritypush notification in a database, queue, or other appropriate datastructure.

At step 1208, mobile device 100 can determine that it is ok to retrylaunching applications associated with stored high priority pushnotifications. For example, mobile device 100 can determine that it isok to retry launching applications when the data, energy and/orattribute budgets have been replenished, device conditions haveimproved, network conditions have improved or other conditions of themobile device 100 have changed, as discussed above in the admissioncontrol description.

At step 1210, mobile device 100 can determine if it is ok to launch anapplication associated with a stored high priority push notification.For example, mobile device 100 can determine if it is ok to launch anapplication based on the criteria discussed above.

At step 1212, mobile device 100 can launch the application in thebackground on the mobile device 100. For example, the application can belaunched as a background process on the mobile device 100 so that theapplication can download updated content from a network resource (e.g.,a content server) on a network (e.g., the internet).

At step 1214, the mobile device 100 can wait a period of time beforepresenting the push notification to the user. For example, the mobiledevice can be configured to allow the application to download contentfor a period of time before notifying the user of the received highpriority push notification.

At step 1216, the mobile device 100 can present the push notification ona user interface of the mobile device 100. For example, the mobiledevice 100 can present a graphical object (e.g., a banner) that includesinformation describing the high priority push notification. The user canselect the graphical object to invoke the application, for example.Since the application had time to download content before the user waspresented with the notification, when the user invokes the applicationthe application will be able to display updated content to the userwithout forcing the user to wait for the updated content to bedownloaded from the network.

Background Uploading/Downloading

FIG. 13 is a block diagram an example system 1300 for performingbackground downloading and/or uploading of data on a mobile device 100.A background download and/or upload can be a network data transfer thatis initiated by an application without explicit input from the user. Forexample, a background download could be performed to retrieve the nextlevel of a video game while the user is playing the video gameapplication. In contrast, a foreground download or upload can be anetwork data transfer performed in response to an explicit indicationfrom the user that the download or upload should occur. For example, aforeground download could be initiated by a user selecting a webpagelink to download a picture, movie or document. Similarly, backgrounduploads can be distinguished from foreground uploads based on whether ornot an explicit user request to upload data to a network resource (e.g.server) was received from the user.

In some implementations, foreground downloads/uploads (e.g.,downloads/uploads explicitly requested by a user) are performedimmediately for the user. For example, the user requesteddownloads/uploads are performed immediately and are not subject tobudgeting constraints or other considerations. Foregrounddownloads/uploads can be performed over a cellular data connection. Incontrast, background downloads and/or uploads can be performedopportunistically and within budgeting constraints and consideringenvironmental conditions, such as the temperature of the mobile device100. For example, a background download or upload can be performed foran attribute or attribute value when the attribute is approved by theadmission control mechanisms described above. In some implementations,background downloads and/or uploads can be restricted to Wi-Fi networkconnections.

In some implementations, system 1300 can include background transferdaemon 1302. In some implementations, background transfer daemon 1302can be configured to perform background downloading and uploading ofdata or content on behalf of applications or processes running on mobiledevice 100. For example background transfer daemon 1302 can performbackground download and/or uploads between application 1304 and server1306 on behalf of application 1304. Thus, the backgrounddownloads/uploads can be performed out of process from application 1304(e.g., not performed in/by the process requesting the download/upload).

In some implementations, application 1304 can initiate a backgrounddownload/upload by sending a request to background transfer daemon 1304to download or upload data. For example, a request to download data(e.g., content) can identify a network location from where the data canbe downloaded. A request to upload data can identify a network locationto which the data can be uploaded and a location where the data iscurrently stored on the mobile device 100. The request can also identifyapplication 1304. Once the request has been made, application 1304 canbe shut down or suspended so that the application will not continueconsuming computing and/or network resources on mobile device 100 whilethe background download/upload is being performed by background transferdaemon 1304.

In some implementations, upon receiving a request to perform abackground upload or download of data, background transfer daemon 1302can send a request to sampling daemon 102 to determine if it is ok forbackground transfer daemon 1302 to perform a data transfer over thenetwork. For example, background transfer daemon 1302 can request thatsampling daemon 102 perform admission control for the data transfer. Inthe admission control request, background transfer daemon 1302 canprovide the identifier (e.g., “bundleId” attribute value) for thebackground transfer daemon 1302 or the identifier for the applicationrequesting the background transfer so that admission control can beperformed on the background transfer daemon or the application. Theadmission control request can include the amount of data to betransferred as the cost of the request to be deducted from thesystem-wide data budget.

In response to receiving the admission control request from backgroundtransfer daemon 1302, sampling daemon 102 can determine if thesystem-wide data and/or energy budgets have been exhausted for thecurrent hour. In some implementations, if sampling daemon 102 determinesthat the mobile device 100 is connected to an external power source,sampling daemon 102 will not prevent a background download/upload basedon the energy budget. Sampling daemon 102 can determine if mobile device100 is connected to Wi-Fi. Sampling daemon 102 can also determinewhether mobile device 100 is in the middle of a thermal event (e.g.,operating temperature above a predefined threshold value). In someimplementations, if sampling daemon 102 determines that the data budgetis exhausted and the mobile device 100 is not connected to Wi-Fi, thatthe energy budget is exhausted and the mobile device 100 is notconnected to an external power source, or that the mobile device 100 isin the middle of a thermal event, then sampling daemon 102 will return a“no” reply to the admission control request by background transferdaemon 1302.

In some implementations, when background transfer daemon 1302 receives a“no” reply to the admission control request from sampling daemon 102,process 1302 can store the background download/upload request fromapplication 1304 in request repository 1308.

In some implementations, sampling daemon 102 can send an retry signal tobackground transfer daemon 1302. For example, sampling daemon 102 cansend the retry signal to background transfer daemon 1302 when the dataand energy budgets are replenished and when the system is no longerexperiencing a thermal event. Sampling daemon 102 can send the retrysignal to background transfer daemon 1302 when the mobile device 100 isconnected to Wi-Fi, connected to external power and when the system isnot experiencing a thermal event. For example, when connected to Wi-Fi,there may not be a need to control data usage. Similarly, when connectedto external power, there may not be a need to conserve battery power.Thus, the data and energy budgets may be disregarded by sampling daemon102 when performing admission control.

In some implementations, when the retry signal is received by backgroundtransfer daemon 1302, background transfer daemon 1302 can send anadmission control request to sampling daemon 102. If sampling daemon 102returns an “ok” reply in response to the admission control request,background transfer daemon 1302 can perform the background download orupload for application 1304. Once a background download is completed,background transfer daemon 1302 can wake or invoke application 1304 andprovide application 1304 with the downloaded data.

In some implementations, background transfer daemon 1302 can notifysampling daemon 102 when the background download/upload starts and endsso that sampling daemon 102 can adjust the budgets and maintainstatistics on the background downloads/uploads performed on mobiledevice 100. For example, background transfer daemon 1302 can send a“backgroundTransfer” attribute start or stop event to sampling daemon102. In some implementations, background transfer daemon 1302 cantransmit the number of bytes (e.g., “system.networkBytes” attributeevent) transferred over cellular data, over Wi-Fi and/or in total sothat sampling daemon 102 can adjust the budgets and maintain statisticson the background downloads/uploads performed on mobile device 100.

In some implementations, sampling daemon 102 can return a timeout valueto background transfer daemon 1302 in response to an admission controlrequest. For example, the timeout value can indicate a period of time(e.g., 5 minutes) that the background transfer daemon has to perform thebackground download or upload. When the timeout period elapses,background transfer daemon 1302 will suspend the background download orupload.

In some implementations, the timeout value can be based on remainingenergy budgets for the current hour. For example, sampling daemon 102can determine how much energy is consumed each second while performing adownload or upload over Wi-Fi based on historical event data collectedby sampling daemon 102. Sampling daemon 102 can determine the time outperiod by dividing the remaining energy budget by the rate at whichenergy is consumed while performing a background download or upload(e.g., energy budget/energy consumed/time=timeout period).

In some implementations, background downloads and/or uploads areresumable. For example, if mobile device 100 moves out of Wi-Fi range,the background download/upload can be suspended (e.g., paused). Whenmobile device 100 reenters Wi-Fi range, the suspended download/uploadcan be resumed. Similarly, if the background download/upload runs out ofenergy budget (e.g., timeout period elapses), the backgrounddownload/upload can be suspended. When additional budget is allocated(e.g., in the next hour), the suspended download/upload can be resumed.

In some implementations, background downloads/uploads can be suspendedbased on the quality of the network connection. For example, even thoughmobile device 100 can have a good cellular data connection betweenmobile device 100 and the servicing cellular tower and a good dataconnection between the cellular tower and the server that the mobiledevice 100 is transferring data to or from, mobile device 100 may nothave a good connection to the server. For example, the transfer ratebetween the mobile device 100 and the server may be slow or thethroughput of the cellular interface may be low. If the transfer rate ofthe background download/upload falls below a threshold transfer ratevalue and/or the throughput of the background download/upload fallsbelow a threshold throughput value, the background download/upload(e.g., data transfer) can be suspended or paused based on the detectedpoor quality network connection until a better network connection isavailable. For example, if a Wi-Fi connection becomes available thesuspended background download/upload can be resumed over the Wi-Ficonnection.

In some implementations, background transfer daemon 1302 can beconfigured with a limit on the number of background downloads and/oruploads that can be performed at a time. For example, backgroundtransfer daemon 1302 can restrict the number of concurrent backgrounddownloads and/or uploads to three.

Example Background Download/Upload Process

FIG. 14 is flow diagram of an example process 1400 for performingbackground downloads and uploads. For example, background downloadsand/or uploads can be performed on behalf of applications on mobiledevice 100 by background transfer daemon 1302.

At step 1402, a background transfer request can be received. Forexample, background transfer daemon 1302 can receive a backgrounddownload/upload request from an application running on mobile device100. Once the application makes the request, the application can beterminated or suspended, for example. The request can identify theapplication and identify source and/or destination locations for thedata. For example, when downloading data the source location can be anetwork address for a server and the destination location can be adirectory in a file system of the mobile device 100. When uploadingdata, the source location can be a file system location and thedestination can be a network location.

At step 1404, mobile device 100 can determine that budgets and deviceconditions do not allow for the data transfer. For example, backgroundtransfer daemon 1302 can ask sampling daemon 102 if it is ok to performthe requested background transfer by making an admission control requestto sampling daemon 102 that identifies the background transfer daemon1302, the application for which the background transfer is beingperformed, and/or the amount of data to be transferred. Sampling daemon102 can determine if energy and data budgets are exhausted and if themobile device 100 is in the middle of a thermal event. If the budgetsare exhausted or if the mobile device 100 is in the middle of a thermalevent, sampling daemon 102 can send a message to background transferdaemon 1302 indicating that it is not ok to perform the background datatransfer (e.g., admission control returns “no”).

At step 1406, mobile device 100 can store the background transferrequest. For example, background transfer daemon 1302 can store thetransfer request in a transfer request repository when sampling daemon102 returns a “no” value in response to the admission control request.

At step 1408, mobile device 100 can determine that it is ok to retry thebackground transfer. For example, sampling daemon 102 can determine thatthe data and energy budgets have been replenished and that the mobiledevice 100 is not in the middle of a thermal event. Sampling daemon 102can send a retry message to background transfer daemon 1302. Backgroundtransfer daemon 1302 can then attempt to perform the requested transfersstored in the transfer request repository by making another admissioncontrol request for each of the stored transfer requests.

At step 1410, mobile device 100 can determine that budgets andconditions of the mobile device 100 allow for background data transfer.For example, background transfer daemon 1302 can ask sampling daemon 102if it is ok to perform the requested background transfer. Samplingdaemon 102 can perform admission control to determine that energy anddata budgets are replenished and that the mobile device 100 is not inthe middle of a thermal event. If the budgets are not exhausted and ifthe mobile device 100 is not in the middle of a thermal event, samplingdaemon 102 can send a message to background transfer daemon 1302indicating that it is ok to perform the background data transfer.

At step 1412, mobile device 100 can perform the background transfer. Forexample, background transfer daemon 1302 can perform the requestedbackground download or background upload for the requesting application.Background transfer daemon 1302 can notify sampling daemon 102 when thebackground transfer begins and ends (e.g., using “backgroundTransfer”attribute start and stop events). Background transfer daemon 1302 cansend a message informing sampling daemon of the number of bytestransferred during the background download or upload (e.g., using the“networkBytes” attribute event). Once the background transfer iscomplete, background transfer daemon 1302 can invoke (e.g., launch orwake) the application that made the background transfer request and sendcompletion status information (e.g., success, error, downloaded data,etc.) to the requesting application.

Enabling/Disabling Background Updates

FIG. 15 illustrates an example graphical user interface (GUI) 1500 forenabling and/or disabling background updates for applications on amobile device. For example, GUI 1500 can be an interface presented on adisplay of mobile device 100 for receiving user input to adjustbackground update settings for applications on mobile device 100.

In some implementations, user input to GUI 1500 can enable or disablebackground updates from being performed for applications based on a userinvocation forecast, as described above. For example, sampling process102 and/or application manager 106 can determine whether backgroundupdates are enabled or disabled for an application and prevent theapplication from being launched by application manager 106 or preventthe application from being included in application invocation forecastsgenerated by sampling daemon 102. For example, if background updates aredisabled for an application, sampling daemon 102 will not include theapplication the user invoked application forecast requested by whenapplication manager 106. Thus, application manager 106 will not launchthe application when background updates are disabled. Conversely, ifbackground updates are enabled for the application, the application maybe included in the application invocation forecast generated by samplingdaemon 102 based on user invocation probabilities, as described above.

In some implementations, user input to GUI 1500 can enable or disablebackground updates from being performed for applications when a pushnotification is received, as described above. For example, samplingdaemon 102, application manager 106 and/or push service daemon 904 candetermine whether background updates are enabled or disabled for anapplication and prevent the application from being launched byapplication manager 106 in response to receiving a push notification.For example, if background updates are disabled for an application and apush notification is received for the application, application manager106 will not launch the application to download updates in response tothe push notification.

In some implementations, GUI 1500 can display applications 1502-1514that have been configured to perform background updates. For example,the applications 1502-1514 can be configured or programmed to run asbackground processes on mobile device 100 when launched by applicationmanager 106. When run as a background process, the applications1502-1514 can communicate with various network resources to downloadcurrent or updated content. The applications 1502-1514 can then updatetheir respective user interfaces to present updated content when invokedby a user of mobile device 100. In some implementations, applicationsthat are not configured or programmed to perform background updates willnot be displayed on GUI 1500.

In some implementations, a user can provide input to GUI 1500 to enableand/or disable background updates for an application. For example, auser can provide input (e.g., touch input) to mobile device 102 withrespect to toggle 1516 to turn on or off background updates forapplication 1502. A user can provide input (e.g., touch input) to mobiledevice 102 with respect to toggle 1518 to turn on or off backgroundupdates for application 1508.

In some implementations, additional options can be specified for abackground update application through GUI 1500. For example, a user canselect graphical object 1510 associated with application 1514 to invokea graphical user interface (not shown) for specifying additionalbackground update options. The background update options can include,for example, a start time and an end time for turning on and/or offbackground updates for application 1514.

Sharing Data Between Peer Devices

FIG. 16 illustrates an example system for sharing data between peerdevices. In some implementations, mobile device 100 can share eventdata, system data and/or event forecasts with mobile device 1600. Forexample, mobile device 100 and mobile device 1600 can be devices ownedby the same user. Thus, it may be beneficial to share information aboutthe user's activities on each device between mobile device 100 andmobile device 1600.

In some implementations, mobile device 1600 can be configured similarlyto mobile device 100, described above. For example, mobile device 1600can be configured with a sampling daemon 1602 that provides thefunctionalities described in the above paragraphs (e.g., attributes,attribute events, forecasting, admission control, etc.).

In some implementations, mobile device 100 and mobile device 1600 can beconfigured with identity services daemon 1620 and identity servicedaemon 1610, respectively. For example, identity services daemon 1620and 1610 can be configured to communicate information between mobiledevice 100 and mobile device 1600. The identity services daemon can beused to share data between devices owned by the same user over variouspeer-to-peer and network connections. For example, identity servicesdaemon 1620 and identity services daemon 1610 can exchange informationover Bluetooth, Bluetooth Low Energy, Wi-Fi, LAN, WAN and/or Internetconnections.

In some implementations, sampling daemon 1602 (and sampling daemon 102)can be configured to share event forecasts and system state informationwith other sampling daemons running on other devices owned by the sameuser. For example, if mobile device 100 and mobile device 1600 are ownedby the same user, sampling daemon 102 and sampling daemon 1602 canexchange event forecast information and/or system status information(e.g., battery status). For example, sampling daemon 1602 can send eventforecast information and/or system status information using identityservices daemon 1610. Identity services daemon 1610 can establish aconnection to identity services daemon 1620 and communicate eventforecast information and/or mobile device 1600 system status informationto sampling daemon 102 through identity services daemon 1620.

In some implementations, application 1608 (e.g., a client of samplingdaemon 1602) can request that sampling daemon 1602 send event forecastsfor a specified attribute or attribute value to sampling daemon 102. Forexample, application 1608 can be an application that is synchronizedwith application 108 of mobile device 100. For example, applications 108and 1608 can be media applications (e.g., music libraries, videolibraries, email applications, messaging applications, etc.) that areconfigured to synchronize data (e.g., media files, messages, statusinformation, etc.) between mobile device 100 and mobile device 1600.

In some implementations, in order to allow a peer device (e.g., mobiledevice 100) determine when to synchronize data between devices,application 1608 can request that sampling daemon 1602 generate temporaland/or peer forecasts for the “bundleId” attribute or a specific“bundleId” attribute value (e.g., the application identifier forapplication 1608) based on attribute event data generated by mobiledevice 1600 and transmit the forecasts to sampling daemon 102. Forexample, a peer device can be remote device (e.g., not the current localdevice) owned by the same user. Mobile device 100 can be a peer deviceof mobile device 1600, for example.

In some implementations, the requesting client (e.g., application 1608)can specify a schedule for delivery and a duration for forecast data.For example, application 1608 can request a peer and/or temporalforecast for the “bundleId” attribute value “mailapp.” Application 1608can request that the forecast be generated and exchanged every week andthat each forecast cover a duration or period of one week, for example.

In some implementations, data exchanges between peer devices can bestatically scheduled. Sampling daemon 1602 can send attribute data thatis necessary for mobile device 100 to have a consistent view of theremote state of mobile device 1600 under a strict schedule (e.g.,application forecasts and battery statistics every 24 hours). In someimplementations, clients can request attribute forecasts or statisticson-demand from the peer device. These exchanges are non-recurring. Therequesting client can be notified when the requested data is received.

In some implementations, sampling daemon 1602 can transmit system statedata for mobile device 1600 to sampling daemon 102. For example,sampling daemon 1602 can receive battery charge level events (e.g.,“batteryLevel” attribute events), battery charging events (e.g.,“cableplugin” events), energy usage events (e.g., “energy” attributeevents) and/or other events that can be used to generate battery usageand charging statistics and transmit the battery-related event data tosampling daemon 102. For example, battery state information can beexchanged every 24 hours. Battery state information can be exchangedopportunistically. For example, when a communication channel (e.g.,peer-to-peer, networked, etc.) is established mobile device 100 andmobile device 1600, the mobile devices can opportunistically use thealready opened communication channel to exchange battery state or othersystem state information (e.g., an identification of the currentforeground application).

As another example, sampling daemon 1602 can receive thermal levelevents (e.g., “thermalLevel” attribute events), network events (e.g.,“networkQuality” attribute events, “networkBytes” attribute events) andtransmit the thermal and/or network events to sampling daemon 102.Sampling daemon 1602 can receive events (e.g., “system.foregroundApp”attribute event) from application manager 106 that indicates whichapplication (e.g., application identifier) is currently in theforeground of mobile device 1600 and transmit the foreground applicationinformation to sampling daemon 102. In some implementations, thermalevents and foreground application change information can be exchangedwith peer devices as soon as the events occur (e.g., as soon as aconnection is established between peer devices). In someimplementations, network status information can be exchanged on aperiodic basis (e.g., once a day, twice a day, every hour, etc.).

Upon receipt of the forecast and/or system event data from samplingdaemon 1602, sampling daemon 102 can store the forecast and/or eventdata in peer data store 1622. Similarly, any forecast and/or event datathat sampling daemon 1602 receives from sampling daemon 102 can bestored in peer data store 1612. In some implementations, forecast and/orevent data received from another device can be associated with device adevice description. For example, the device description can include adevice name, a device identifier and a model identifier that identifiesthe model of the device. The device description can be used to lookupforecast data and/or event data for the device in peer data store 1622.Once mobile device 100 and mobile device 1600 have exchanged forecastand/or event data, the mobile devices can use the exchanged informationto determine when to communicate with each other using the remoteadmission control mechanism below. By allowing devices to shareinformation only when the information is needed and when the batterystate of the devices can support sharing the information, powermanagement of communications can be improved.

Remote Admission Control

In some implementations, mobile device 100 (or mobile device 1600) canperform admission control based on data received from another device.For example, sampling daemon 102 can perform admission control based onforecast and system event data received from sampling daemon 1602 andstored in peer data store 1622. For example, to synchronize data withapplication 1608, application 108 can send a synchronization message toidentity services daemon 1620. For example, the synchronization messagecan include an identifier for mobile device 100, an identifier formobile device 1600, a priority identifier (e.g., high, low), and amessage payload (e.g., data to be synchronized).

Low Priority Messages

In some implementations, a low priority message can be transmitted aftergoing through admission control. For example, a low priority message canbe a message associated with discretionary processing (e.g., backgroundapplications, system utilities, anticipatory activities, activities thatare not user-inititated). For example, identity services daemon 1620 cansend an admission control request to sampling daemon 102 for a“bundleId” attribute value that is the bundle identifier for application1608 (e.g., “bundleId”=“1608”). In addition to the “bundleId” attributename and value (e.g., “1608”), identity services daemon 1620 can providethe device name (e.g., “device 1600”) in the admission control requestto indicate that application 108 is requesting admission control forcommunication with another device.

In some implementations, in response to receiving the admission controlrequest, sampling daemon 102 can perform local admission control andremote admission control. For example, sampling daemon 102 can performlocal admission control, as described above, to determine if mobiledevice 100 is in condition to allow an event associated with thespecified attribute value (e.g., “bundleId”=“1608”) to occur. Samplingdaemon 102 can check local energy, data and attribute budgets, forexample, and ask for voter feedback to determine whether mobile device100 is in condition to allow an event associated with the specifiedattribute value (e.g., “bundleId”=“1608”).

In addition to performing local admission control, sampling daemon 102can perform remote admission control based on the “bundleId” attributeforecasts, event data and system data received from mobile device 1600and stored in peer data store 1622. For example, sampling daemon 102 canuse the device identifier (e.g., “device 1600”, device name, uniqueidentifier, UUID, etc.) to locate data associated with mobile device1600 in peer data store 1622. Sampling daemon 102 can analyze theattribute (e.g., “bundleId”) forecast data received from sampling daemon1602 to determine if application 1608 is likely to be invoked by theuser on mobile device 1600 in the current 15-minute timeslot. Ifapplication 1608 is not likely to be invoked by the user in the current15-minute timeslot, then sampling daemon 102 can return a “no” value inresponse to the admission control request. For example, by allowingapplication 108 to synchronize with application 1608 only whenapplication 1608 is likely to be used on mobile device 1600, samplingdaemon 102 can delay the synchronization process and conserve systemresources (e.g., battery, CPU cycles, network data) until such time asthe user is likely to use application 1608 on mobile device 1600.

In some implementations, if application 1608 is likely to be invoked bythe user of mobile device 1600 in the current 15-minute timeslot, thensampling daemon 102 can check the system data associated with mobiledevice 1600 and stored in peer data store 1622. For example, samplingdaemon 102 can check the system data associated with mobile device 1600to determine if mobile device 1600 has enough battery charge remainingto perform the synchronization between application 108 and application1608. For example, sampling daemon 102 can check if there is currentlyenough battery charge to complete the synchronization betweenapplication 108 and application 1608. Sampling daemon 102 can check ifthere is enough battery charge to perform the synchronization andcontinue operating until the next predicted battery recharge (e.g.,“cablePlugin” attribute event). For example, sampling daemon 102 cangenerate a temporal forecast for the “cablePlugin” attribute thatidentifies when the next “cablePlugin” attribute event is likely tooccur. Sampling daemon 102 can analyze energy usage statistics (events)to predict energy usage until the next “cablePlugin” event and determineif there is enough surplus energy to service the synchronizationtransmission between application 108 and application 1608. If samplingdaemon 102 determines that mobile device 1600 does not have enoughenergy (e.g., battery charge) to service the synchronization, samplingdaemon 102 can return a “no” value in response to the remote admissioncontrol request.

In some implementations, sampling daemon 102 can check the system dataassociated with mobile device 1600 to determine if mobile device 1600 isin a normal thermal condition (e.g., not too hot) and can handleprocessing the synchronization request. For example, if “thermalLevel”attribute event data received from mobile device 1600 indicates thatmobile device 1600 is currently operating at a temperature above athreshold value, sampling daemon 102 can prevent the synchronizationcommunication by returning a “no” value in response to the remoteadmission control request.

In some implementations, when the forecast data indicates that the useris likely to invoke application 1608 on mobile device 1600 and theenergy, thermal and other system state information indicate that mobiledevice 1600 is in condition to handle a communication from mobile device100, sampling daemon 102 can return a “yes” value to identity servicesdaemon 1620 in response to the admission control request. In response toreceiving a “yes” value in response to the admission control request,identity services daemon 1620 can transmit the synchronization messagefor application 108 to identity services daemon 1610 on mobile device1600. Application 108 and application 1608 can then synchronize data byexchanging messages through identity services daemon 1620 and identityservices daemon 1610.

In some implementations, a high priority message can be transmittedafter going through remote admission control. For example, a highpriority message can be a message associated with a user-initiated task,such as a message associated with a foreground application or a messagegenerated in response to a user providing input. In someimplementations, admission control for high priority messages can behandled similarly to low priority messages. However, when performingremote admission control for high priority messages, a high prioritymessage can be admitted (allowed) without considering attribute forecastdata (e.g., “bundleId” forecast data) because the high priority messageis typically triggered by some user action instead of being initiated bysome discretionary background task.

In some implementations, when performing admission control for highpriority messages, the battery state of the remote device (e.g., mobiledevice 1600) can be checked to make sure the remote device (e.g., peerdevice) has enough battery charge available to process the high prioritymessage. If there is enough battery charge available on the remotedevice, then the high priority message will be approved by remoteadmission control. For example, sampling daemon 102 can transmit a “yes”value to identity services daemon 1620 in response to the remoteadmission control request when there is enough battery charge remainingto process the high priority message. If there is not enough batterycharge available on the remote device, then the high priority messagewill be rejected by remote admission control. For example, samplingdaemon 102 can transmit a “no” value to identity services daemon 1620 inresponse to the remote admission control request when there is enoughbattery charge remaining to process the high priority message. Thus,identity services daemon 1620 will initiate communication with a peerdevice (e.g., mobile device 1600) when the peer device has enoughbattery charge remaining to process the message in question.

In some implementations, when a sampling daemon 102 is notified of ahigh priority message, sampling daemon 102 can send current batterystate information (e.g., current charge level) to identity servicesdaemon 1620. Identity services daemon 1620 can then add the batterystate information to the high priority message. Thus, system stateinformation can be efficiently shared between devices by piggy backingthe battery state information (or other information, e.g., thermallevel, foreground application, etc.) on other messages transmittedbetween mobile device 100 and mobile device 1600.

In some implementations, sampling daemon 102 can send a retry message toidentity services daemon 1620. For example, when conditions on mobiledevice 100 or mobile device 1600 change (e.g., battery conditionsimprove), sampling daemon 102 can send identity services daemon 1620 aretry message. In some implementations, a retry message can be generatedwhen the remote focal application changes. For example, if the user onthe remote peer device is using the “mailapp” application, the “mailapp”application becomes the focal application. When the user begins usingthe “webbrowser” application, the focal application changes to the“webbrowser” application. The change in focal application can bereported as an event to sampling daemon 1602 and transmitted to samplingdaemon 102 when peer data is exchanged between mobile device 100 andmobile device 1600. Upon receiving the event information indicating achange in focal application at the peer device 1602, sampling daemon 102can send a retry message to identity services daemon 1620. Identityservices daemon 1620 can then retry admission control for each messagethat was rejected by sampling daemon 102. For example, identity servicesdaemon 1620 can store rejected messages (e.g., transmission tasks) andsend the rejected messages through admission control when a retrymessage is received from sampling daemon 102. In some implementations,rejected messages can be transmitted after a period of time has passed.For example, a message that has not passed admission control can be sentto the peer device after a configurable period of time has passed.

In some implementations, identity services daemon 1620 can interrupt adata stream transmission when sampling daemon 102 indicates thatconditions on mobile device 100 or mobile device 1600 have changed. Forexample, if sampling daemon 102 determines that battery conditions onmobile device 100 or mobile device 1600 have changed such that one ofthe mobile devices may run out of battery power, sampling daemon 102 cantell identity services daemon 1620 to stop transmitting and retryadmission control for the attribute event associated with the datastream.

Process for Sharing Data Between Peer Devices

FIG. 17 illustrates an example process 1700 for sharing data betweenpeer devices. Additional details for process 1700 can be found abovewith reference to FIG. 16. At step 1702, a mobile device can receiveevent data from a peer device. For example, event data can be shared as“digests” (e.g., forecasts, statistics, etc.) or as raw (e.g.,unprocessed) event data. For example, a second device (e.g., mobiledevice 1600) is a peer device of the mobile device 100 when the seconddevice and the mobile device are owned by the same user. The mobiledevice 100 can receive event data related to system state (e.g., batterystate, network state, foreground application identifier, etc.) of mobiledevice 1600. The mobile device can receive attribute event forecasts,statistics, or raw event data from the mobile device 1600 based onevents that have occurred on mobile device 1600. For example, anapplication 1608 on the peer device 1600 can instruct the samplingdaemon 1602 on the peer device 1600 to generate and send forecasts for aparticular attribute or attribute value to the mobile device 100.

At step 1704, an identity services daemon 1620 on the mobile device 100can receive a message to transmit to the peer device 1600. For example,an application 108 running on the mobile device may need to share,exchange or synchronize data with a corresponding application 1608 onthe peer device 1600. The application 108 can send a message containingthe data to be shared to the identity services daemon 1620.

At step 1706, the sampling daemon 102 on the mobile device 100 candetermine whether to transmit the message based on data received fromthe peer device 1600. For example, the sampling daemon 102 can perform alocal admission control check and a remote admission control check todetermine whether the message should be sent to the peer device 1600 atthe current time. If the attribute event forecasts received from thepeer device 1600 indicate that the user of peer device 1600 is likely toinvoke application 1608 at the current time and if the event dataindicates that the conditions (e.g., battery state, thermal level, etc.)of peer device 1600 are such that initiating communication with peerdevice 1600 will not deplete the battery or make the thermal stateworse, then sampling daemon 102 can approve the transmission of themessage.

At step 1708, once sampling daemon 102 performs admission control andapproves initiating communication with the peer device 1600, identityservices daemon 1620 can transmit the message to the peer device 1600.For example, identity services daemon 1620 can transmit the message toidentity services daemon 1610 of peer device 1600. Identity servicesdaemon 1610 can then transmit the message to application 1608 so thatapplication 108 and application 1608 can synchronize data.

Example System Architecture

FIG. 18 is a block diagram of an example computing device 1800 that canimplement the features and processes of FIGS. 1-17. Computing device1800 can be a smartphone, a tablet computer, a laptop computer, awearable device (e.g., a watch), a set top box, or a vehicle mediasystem, for example. The computing device 1800 can include a memoryinterface 1802, one or more data processors, image processors and/orcentral processing units 1804, and a peripherals interface 1806. Thememory interface 1802, the one or more processors 1804 and/or theperipherals interface 1806 can be separate components or can beintegrated in one or more integrated circuits. The various components inthe computing device 1800 can be coupled by one or more communicationbuses or signal lines.

Sensors, devices, and subsystems can be coupled to the peripheralsinterface 1806 to facilitate multiple functionalities. For example, amotion sensor 1810, a light sensor 1812, and a proximity sensor 1814 canbe coupled to the peripherals interface 1806 to facilitate orientation,lighting, and proximity functions. Other sensors 1816 can also beconnected to the peripherals interface 1806, such as a global navigationsatellite system (GNSS) (e.g., GPS receiver), a temperature sensor, abiometric sensor, magnetometer or other sensing device, to facilitaterelated functionalities.

A camera subsystem 1820 and an optical sensor 1822, e.g., a chargedcoupled device (CCD) or a complementary metal-oxide semiconductor (CMOS)optical sensor, can be utilized to facilitate camera functions, such asrecording photographs and video clips. The camera subsystem 1820 and theoptical sensor 1822 can be used to collect images of a user to be usedduring authentication of a user, e.g., by performing facial recognitionanalysis.

Communication functions can be facilitated through one or more wirelesscommunication subsystems 1824, which can include radio frequencyreceivers and transmitters and/or optical (e.g., infrared) receivers andtransmitters. The specific design and implementation of thecommunication subsystem 1824 can depend on the communication network(s)over which the computing device 1800 is intended to operate. Forexample, the computing device 1800 can include communication subsystems1824 designed to operate over a GSM network, a GPRS network, an EDGEnetwork, a Wi-Fi or WiMax network, and a Bluetooth™ network. Inparticular, the wireless communication subsystems 1824 can includehosting protocols such that the device 100 can be configured as a basestation for other wireless devices.

An audio subsystem 1826 can be coupled to a speaker 1828 and amicrophone 1830 to facilitate voice-enabled functions, such as speakerrecognition, voice replication, digital recording, and telephonyfunctions. The audio subsystem 1826 can be configured to facilitateprocessing voice commands, voiceprinting and voice authentication, forexample.

The I/O subsystem 1840 can include a touch-surface controller 1842and/or other input controller(s) 1844. The touch-surface controller 1842can be coupled to a touch surface 1846. The touch surface 1846 andtouch-surface controller 1842 can, for example, detect contact andmovement or break thereof using any of a plurality of touch sensitivitytechnologies, including but not limited to capacitive, resistive,infrared, and surface acoustic wave technologies, as well as otherproximity sensor arrays or other elements for determining one or morepoints of contact with the touch surface 1846.

The other input controller(s) 1844 can be coupled to other input/controldevices 1848, such as one or more buttons, rocker switches, thumb-wheel,infrared port, USB port, and/or a pointer device such as a stylus. Theone or more buttons (not shown) can include an up/down button for volumecontrol of the speaker 1828 and/or the microphone 1830.

In one implementation, a pressing of the button for a first duration candisengage a lock of the touch surface 1846; and a pressing of the buttonfor a second duration that is longer than the first duration can turnpower to the computing device 1800 on or off. Pressing the button for athird duration can activate a voice control, or voice command, modulethat enables the user to speak commands into the microphone 1830 tocause the device to execute the spoken command. The user can customize afunctionality of one or more of the buttons. The touch surface 1846 can,for example, also be used to implement virtual or soft buttons and/or akeyboard.

In some implementations, the computing device 1800 can present recordedaudio and/or video files, such as MP3, AAC, and MPEG files. In someimplementations, the computing device 1800 can include the functionalityof an MP3 player, such as an IPod™.

The memory interface 1802 can be coupled to memory 1850. The memory 1850can include high-speed random access memory and/or non-volatile memory,such as one or more magnetic disk storage devices, one or more opticalstorage devices, and/or flash memory (e.g., NAND, NOR). The memory 1850can store an operating system 1852, such as Darwin, RTXC, LINUX, UNIX,OS X, WINDOWS, or an embedded operating system such as VxWorks.

The operating system 1852 can include instructions for handling basicsystem services and for performing hardware dependent tasks. In someimplementations, the operating system 1852 can be a kernel (e.g., UNIXkernel). In some implementations, the operating system 1852 can includeinstructions for performing dynamic adjustment of the mobile devicebased on user activity. For example, operating system 1852 can implementthe dynamic adjustment features as described with reference to FIGS.1-17.

The memory 1850 can also store communication instructions 1854 tofacilitate communicating with one or more additional devices, one ormore computers and/or one or more servers. The memory 1850 can includegraphical user interface instructions 1856 to facilitate graphic userinterface processing; sensor processing instructions 1858 to facilitatesensor-related processing and functions; phone instructions 1860 tofacilitate phone-related processes and functions; electronic messaginginstructions 1862 to facilitate electronic-messaging related processesand functions; web browsing instructions 1864 to facilitate webbrowsing-related processes and functions; media processing instructions1866 to facilitate media processing-related processes and functions;GNSS/Navigation instructions 1868 to facilitate GNSS andnavigation-related processes and instructions; and/or camerainstructions 1870 to facilitate camera-related processes and functions.

The memory 1850 can store other software instructions 1872 to facilitateother processes and functions, such as the dynamic adjustment processesand functions as described with reference to FIGS. 1-17.

The memory 1850 can also store other software instructions 1874, such asweb video instructions to facilitate web video-related processes andfunctions; and/or web shopping instructions to facilitate webshopping-related processes and functions. In some implementations, themedia processing instructions 1866 are divided into audio processinginstructions and video processing instructions to facilitate audioprocessing-related processes and functions and video processing-relatedprocesses and functions, respectively.

Each of the above identified instructions and applications cancorrespond to a set of instructions for performing one or more functionsdescribed above. These instructions need not be implemented as separatesoftware programs, procedures, or modules. The memory 1850 can includeadditional instructions or fewer instructions. Furthermore, variousfunctions of the computing device 1800 can be implemented in hardwareand/or in software, including in one or more signal processing and/orapplication specific integrated circuits.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

What is claimed is:
 1. A method comprising: receiving, at a thermalmanagement daemon executing on a mobile device, a request to vote onallowing an event to occur that is associated with a specified value ofan attribute, where the request is received at a first time; requesting,from a sampling daemon executing on the mobile device, a peer forecastfor the attribute during a time period of interest corresponding to thefirst time, where the peer forecast includes scores for each of aplurality of values associated with the attribute, the score for each ofthe plurality of values indicating the relative likelihood of each ofthe plurality values for the attribute occurring during the time periodof interest; receiving the peer forecast including the scores for eachof the plurality of values, including the specified attribute value,associated with the attribute and predicted to occur near the firsttime; voting to allow the event based on the score of the specifiedattribute value.
 2. The method of claim 1, further comprising:determining a number of highest scored attribute values in the pluralityof values; voting to allow the event when the specified attribute valueis included in the number of highest scored attribute values.
 3. Themethod of claim 1, further comprising: voting to prevent the event whenthe specified attribute value is not included in the plurality ofvalues.
 4. The method of claim 1, further comprising: determining anumber of lowest scored attribute values in the plurality of values;voting to prevent the event when the specified attribute value isincluded in the number of lowest scored attribute values.
 5. The methodof claim 4, further comprising: determining the number of lowest scoredattribute values based on a current operating temperature of the mobiledevice.
 6. The method of claim 5, further comprising: determining thenumber of lowest scored attribute values based on where the currentoperating temperature is in a range of operating temperatures.
 7. Anon-transitory computer-readable medium including one or more sequencesof instructions which, when executed by one or more processors, cause:receiving, at a thermal management daemon executing on a mobile device,a request to vote on allowing an event to occur that is associated witha specified value of an attribute; requesting, from a sampling daemonexecuting on the mobile device, a peer forecast for the attribute duringa time period of interest corresponding to the first time, where thepeer forecast includes scores for each of a plurality of valuesassociated with the attribute, the score for each of the plurality ofvalues indicating the relative likelihood of each of the pluralityvalues for the attribute occurring during the time period of interest;receiving the peer forecast including the scores for each of theplurality of values including the specified attribute value associatedwith the attribute and predicted to occur near the first time; voting toallow the event based on the score of the specified attribute value. 8.The non-transitory computer-readable medium of claim 7, furthercomprising: determining a number of highest scored attribute values inthe plurality of values; voting to allow the event when the specifiedattribute value is included in the number of highest scored attributevalues.
 9. The non-transitory computer-readable medium of claim 7,wherein the instructions cause: voting to prevent the event when thespecified attribute value is not included in the plurality of values.10. The non-transitory computer-readable medium of claim 7, wherein theinstructions cause: determining a number of lowest scored attributevalues in the plurality of values; voting to prevent the event when thespecified attribute value is included in the number of lowest scoredattribute values.
 11. The non-transitory computer-readable medium ofclaim 10, wherein the instructions cause: determining the number oflowest scored attribute values based on a current operating temperatureof the mobile device.
 12. The non-transitory computer-readable medium ofclaim 11, wherein the instructions cause: determining the number oflowest scored attribute values based on where the current operatingtemperature is in a range of operating temperatures.
 13. A systemcomprising: one or more processors; and a non-transitorycomputer-readable medium including one or more sequences of instructionswhich, when executed by one or more processors, cause: receiving, at athermal management daemon executing on a mobile device, a request tovote on allowing an event to occur that is associated with a specifiedvalue of an attribute, where the request is received at a first time;requesting, from a sampling daemon executing on the mobile device, apeer forecast for the attribute during a time period of interestcorresponding to the first time, where the peer forecast includes scoresfor each of a plurality of values associated with the attribute, thescore for each of the plurality of values indicating the relativelikelihood of each of the plurality values for the attribute occurringduring the time period of interest; receiving the peer forecastincluding the scores for each of the plurality of values, including thespecified attribute value, associated with the attribute and predictedto occur near the first time; voting to allow the event based on thescore of the specified attribute value.
 14. The system of claim 13,further comprising: determining a number of highest scored attributevalues in the plurality of values; voting to allow the event when thespecified attribute value is included in the number of highest scoredattribute values.
 15. The system of claim 13, wherein the instructionscause: voting to prevent the event when the specified attribute value isnot included in the plurality of values.
 16. The system of claim 13,wherein the instructions cause: determining a number of lowest scoredattribute values in the plurality of values; voting to prevent the eventwhen the specified attribute value is included in the number of lowestscored attribute values.
 17. The system of claim 16, wherein theinstructions cause: determining the number of lowest scored attributevalues based on a current operating temperature of the mobile device.18. The system of claim 17, wherein the instructions cause: determiningthe number of lowest scored attribute values based on where the currentoperating temperature is in a range of operating temperatures.